Economists Whose Idea Are Changing The World

Economics is one of the most important and influential fields of study one can enter: the ramifications of its theory have changed our world, and will of course continue to do so. When we think of powerful men of the past, our minds turn to politicians and leaders the Churchills, Ghandis and even the Stalins and Hitlers who led men to glory, freedom, or destructions. However, the economists on whose ideas and counsel these giants of the modern age based their policies have had no less of an influence, indeed arguably even more of an impact, on our world.

We’re living in the age of the rock star academic. Everyone is trying to make sense of financial crises and the old economics textbooks don’t work so well anymore. So it’s natural to turn to the people who study this stuff for a living. Thomas Piketty, a French academic, sold 1.5 million copies of his book “Capital in the Twenty-First Century,” while Nobel prize-winning economists like Paul Krugman and Joseph Stiglitz can be found burning up social media, the newspapers, and the conference circuit. But not everyone with influential ideas on economics and finance is as well-known. Here are the Economists that are changing the world behind-the-scenes.

Related post: Skill And knowledge Required For An Economics Major:

1: Ha-Joon Chang, University of Cambridge

Idea: Developed countries talk a lot about the free market but really use their power and financial strength to profit at the expense of emerging economies. Chang’s ideas are controversial, centering on the role that international bodies like the IMF and World Bank play in the world economy. In books such as Kicking Away the Ladder and The Myth of Free Trade he argues that the governments of bigger economies help out their own companies, while preaching the benefits of the free market to developing nations.

2: Katherina Pistor, Columbia Law School

Idea: The rule of law must be suspended for financial markets in a crisis, or the whole system will collapse. Pistor, who won the Max Planck academic research award in 2012, is developing a legal theory of finance to work out how laws affect its shape and composition. She discovered that, in a crisis, the regulations that build markets aren’t worth the paper they’re printed on. Political power is the driving force behind who gets hit in the heat of the moment.

3: Charles Calomiris, Columbia Business School

Idea: Financial collapses don’t happen at random and aren’t inevitable. They come from complex bargains between politicians and bankers that spiral out of the government’s control. That’s one of the reasons why the US has had 12 major banking crises since 1840, while Canada has had none.

Related post: Economics Books For Economist.

4: Jon Danielsson, London School of Economics

Idea: Trusting your risk models will lose you money in a crisis. Risk models will generally tend to have the same outcomes when everything is going well, even if they have different mathematical foundations. This tricks people in to thinking that they work all the time. But when all hell breaks loose, the models will give you wildly different risk assesments, leaving you flying blind. This is bad for banks and hedge funds but even worse for central banks, who have to make policy decisions for everyone else.

5: Marianne Bertrand, University of Chicago Booth

Idea: CEOs are rewarded for luck rather than performance. Also, employers judge applicants on their name as much as their qualifications. Bertrand is one the reasons why there’s been such a shareholder backlash against CEO pay, after proving their huge bonuses are based on luck rather than genius. In a 2003 paper, she and Sendhil Mullainathan also famously replied to help-wanted ads in Chicago and Boston with fake names. Some applicants used names like Emily and Greg, while others used names like Lakisha and Jamal. “The results show significant discrimination against African-American names,” the authors wrote. “White names receive 50% more callbacks for interviews.”

Related post: Highest Paying Jobs with an Economics Degree

6: Alvin Roth, Harvard University and Stanford University

Idea: You don’t need money to make a stable market for something. Roth, along with Lloyd Shapely, won the Nobel Prize in 2012 for showing that people can make a market based on mutually-beneficial swaps rather than cash to satisfy a specific need. This was particularly useful for easing the shortage of kidney donors in the US. Roth used game theory to pair up donors with patients they didn’t know, making it easier for people to swap their organs and find a match.

7: Richard Portes, London Business School

Idea: Bondholders can often work together to get concessions from a borrower. Portes, now professor of economics at London Business School, laid down the groundwork for collective action clauses, where sovereign bondholders use their bargaining power to impose conditions on a debtor country. The work has been especially important in cases like Greece or Argentina.

Related post: 11 Best Modern Economics Books

8: Charles Goodhart, London School of Economics

Idea: Goodhart’s Law. Goodhart said that as soon as governments or central banks turn a statistic, such as the stock market, into an implicit policy target, it ceases to become a reliable statistic. This is because players in financial markets change their investment strategies to pre-empt the policy. Goodhart was one of the orignal members of the Bank of England’s monetary policy committee in 1997, and a veteran of financial crises in 1970s.

9: Alberto Alesina, Harvard University

Idea: Far from hurting growth, austerity measures can actually help economies recover. In 2009, Alesina and Silvia Ardegna published a paper called Large Changes in Fiscal Policy: Taxes Versus Spending. It was an important part of the debate in the years that followed over whether austerity and reducing debt or boosting government spending were the best strategies for economies recovering, cited by fiscal hawks.

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November 14, 2020 by Economy

6 REASONS WHY YOU SHOULD BE ECONOMIST

A question or a challenge? There is a variety of reasons a student would choose to major in any discipline. These choices are inspired by different reasons, from family background to the environment or peer group association or it could even be for a choice of career or passion, whichever it is, it is important for you to understand some basic insight about economics.

From a personal perspective, the study of economics has provided me with a systematic framework for analyzing, researching, writing, and teaching about a wide array financial and regional economic issues. Economics has provided me with a methodology for understanding and making sense of our complex environment. So here are 6 absolutely foolproof reasons for studying economics.

1. Economic Forecaster:

As an economist, you can make a living from predicting future economic events. The key to being a good economic forecaster is to use a mixture of dice and lottery numbers. (some economists make the mistake of using just lottery numbers, but this can lead to really bad forecasting) If this method fails just use the statistics from the previous year; they are always more accurate than the actual predictions of economists. An economist practically evaluates risks, which may be conditions or circumstances that may lead to a result of fluctuating from their initial estimates, hence demonstrates the thorough and deep thinking process used to final forecast estimates. Forecast results are sometimes generated annually but at other times updated frequently. Though there are so many tools put in place to help economists achieve results but need the statistical knowledge and models to follow in order to arrive at the result for particular variables. For your information economists have successfully predicted 10 out of the last 2 recessions.

2. You will understand the Market dynamics:

learning, as they say, is all-round progress, which touches every part of one’s life. Choosing to study economics will help you to understand the dynamics of the market. Market dynamics are simply those factors that impact the market. An economist perspective would mean demand and supply, opportunity cost, scarcity, equilibrium just to mention a few. The course will expand your vocabulary and knowledge to understand how the market works even if you would not be working primarily as an economist, but always at the back of your mind to help understand your organization market and can also help to influence the strategic decision in improving your organization’s performance.

3. Economists know reasons for unemployment:

Economist would define unemployment as a part of the labour force actively seeking employment. Dividing the unemployed over the employed gives an economist a statistically calculated percentage. An indicator used in understanding the operations of the total country’s labour force. Unemployment consequentially has an adverse effect on a country’s economy, especially when the rates are so high. This can then draw the attention of the media and other nations of the world too. There are numerous reasons for unemployment of a particular person in a country but if a country experiences recession or economic fall, most of the available private sector may be forced to lay off staff to reduce cost, and this, in turn, is causative for sometimes the mental minds both for the unemployed and employed. Reasons are numbers to an economist, which they could also predict or decipher in an economy.

Related post: Economics Books For Economist.

4. Able to make a good decision on personal spending:

There is a funny idea that economists are stingy people, but it is not so, they are only after making a very good economic decision. Learning to major in this course would enlighten your scope of reasoning to another level and eventually with enough passion to carry on would turn into a habit that is economically sound and financially healthy, since economics will teach you about market behaviours and organization trends. For example, learning about willingness to pay theory could help you develop your own spending habits, which will prove your sound economic mind and able to influence analytical thinking in immediate family members if possible.

5. Economists earn a high paying Job:

This is another reason be it as it may, why some students study economics as a major. It gives you the power to examine the labour markets, prospective private companies, industry tendencies or forces which direct the economy as it is. Definitely a major in economics could land you different jobs, one of such is a market research analyst, where they are required to apply skills like graphical representation, statistical skills and a critical mind for thinking, another is an economic consultant is needed across various sectors like government, finance, education, healthcare and business, they are required also to analyze and research economic strategies in order to help enhance performance. Any student that enjoys analytical thinking could major in economics which in turn will help to understand deeper how to coordinate and interpret data using mathematical formulas and statistics to make calculations. There are models also in place to learn, which helps to predict the effect of policy decisions, industry tendencies, climate change, investment, just to mention a few. The ability for problem-solving and great communication skills should be a strong suit for such an interested student, they are required to evaluate problems and recommend solutions.

Related post: Highest Paying Jobs with an Economics Degree

6. You can always give advice.

When the economy enters a recession, you will be able to tell everybody why the economy is in a recession. Also, you will be able to suggest several conflicting reasons as to how we can get out of a recession. This will simultaneously, both confuse and impress everybody; but it doesn’t matter because nobody ever listen to economists.

Related post: Skill And knowledge Required For An Economics Major:

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October 29, 2020 by Economy

Different Ways To Calculate GDP Of A Country

The gross domestic product (GDP) of a nation is an estimate of the total value of all the goods and services it produced during a specific period, usually a quarter or a year. Its greatest use is as a point of comparison: Did the nation’s economy grow or contract compared to the previous period measured?

There are three different methods (Expenditure, Income and Production) which can be used to measure the GDP of a country. All of these methods in theory should sum to the same amount.

1. Expenditure method

The expenditure approach is where you add up all the various types of spending which occurs within an economy. There are 4 different types.

Consumption (C)

Consumption is all the spending that households do on goods and services. For example, the number of apples a household purchases; the amount of money spent on healthcare; the amount of money spent purchasing new cars and the money spent on pizza are all examples of consumption spending.

Investment (I)

Investment is the spending that firms do machinery and equipment to operate their businesses. Examples of investment spending would be a mining company purchases a truck to transport coal; It companies purchasing new computers and the purchase of a new plane for an airline company.

Government Spending (G)

Government spending is the spending that the government conducts within an economy. Examples of government spending include spending on defence; spending on health care; the building of roads and education spending.

Net Exports (NX)

Net exports is defined at the purchases of domestically produced goods by foreigners subtracted from the purchases of internationally produced goods by local residents. In essence, it is the value of what is sent overseas minus the value of stuff that comes here.

If an airline company operating in the USA purchases a new plane from France, this would be considered an import for the USA and an export for France. This would cause the net exports to decrease for the USA whilst causing the net exports to increase for France.

An interesting case is where a foreign student from China comes and studies at a school in the USA. This is considered an export from the USA to China since the USA is producing a service (education) which is essentially being “sent” to a Chinese student who is from the Chinese economy. Thus, China is importing education from the USA.

Therefore, if we add up these 4 components we get:

GDP = C + I + G +NX

This is also called the demand approach to calculating GDP since all these components are demands for goods and services. It is looking at the demand side of the economy.

For example, using the input-output tables for Australia you can calculate the GDP for Australia in the year 2018 with:

C=$969,173C=$969,173
I=$418,703I=$418,703
G=$309,325G=$309,325
X=$308,306X=$308,306
M=$357,121M=$357,121

Giving GDP=$969,173+$418,703+$309,325+$308,306−$357,121GDP=$969,173+$418,703+$309,325+$308,306−$357,121

GDP=$1,659,604GDP=$1,659,604

where GDP is measured in millions of dollars.

2. Income Method :

The income approach starts with the income earned from the production of goods and services. Under the income approach, we calculate the income earned by all the factors of production in an economy.

Factors of production are the inputs which go into producing final product or service. Thus, the factors of production for business are – Land, Labour, Capital and Management within the domestic boundaries of a country. 

In this approach, we calculate income from each of these Factor of production which includes the wages got by labour, the rent earned by land, the return on capital in the form of interest, as well as business profits earned by management. Sum of All these incomes constitutes national income and is a way to calculate GDP.

Formula : Net National Income = Wages + Rent + Interest + Profits

To make it gross, we need to do two adjustments – Add depreciation of capital & Add Net Foreign Factor Income. NFFI is (income earned by the rest of the world in the country – income earned by the country from the rest of the world)

GDP (Factor Cost) = Wages + Rent + Interest + Profits+ Depreciation + Net Foreign Factor Income

This basically is the sum of final income of all factors of production contributing to a business in a country before tax.

Now if we add taxes and deduct subsidies, then it becomes GDP at Market cost.

GDP (Market Cost) = GDP (Factor Cost)+ (Indirect Taxes – Subsidies)

3. Production Method:

The production method (or value-added) is where we calculate the total value of all goods produced in the economy minus the value of intermediate goods.

Consider an economy which produces steel and cars. Suppose the economy produces 100 units of steel which it sells for $1 and it produces 10 cars, using 5 units of steel, which it sells for $100.

As the production of steel requires no intermediate inputs, the value added from the production of steel is $100.

The production of cars produces $1000 worth of cars using $50 of steel. Therefore, the value added is $950.

The total value-added/GDP of the economy is thus $1050. Alternatively, we could have added the total amount spent on the cars $1000 and total spend on steel $100 giving $1100 and then subtracted the $50 of intermediate inputs to also get $1050.

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October 13, 2020 by Economy

Data Scientist vs Data Analyst vs Data Engineer

Data engineer, data analyst and data scientist are job titles you’ll often hear mentioned together when people are talking about the fast-growing field of data science. Of course, there are plenty of other job titles in data science, but here, we’ll talk about these three primary roles,  how they differ from one another, and which position might be best for you. Although each company may have its own definitions for each position, there are big differences between what you might be doing each day as a data analyst, data scientist, or data engineer. We’re going to dig into each of these specific roles in more depth.

Data Scientist vs Data Analyst vs Data Engineer: Job Role, Skills, and Salary

Data Scientist vs Data Analyst vs Data Engineer
Data Scientist vs Data Analyst vs Data Engineer

Data Scientist 

They use advanced data techniques such as clustering, neural networks, decision trees, and the like for deriving business insights. In this role, you will be the senior-most in a team and should have deep expertise in machine learning, statistics, and data handling. You will be responsible for developing actionable business insights after they get inputs from Data Analysts and Data Engineers. You should have the skill set of both a data analyst and a data engineer. However, in the case of a data scientist, the skill sets need to be more in-depth and exhaustive.

The Required Skillsets

Coding skills are central to each of these job roles – data scientists need to have mastery over programming languages like Java, Python, SQL, R, and SAS, to name a few. Additionally, you need a working knowledge of Big Data frameworks like Hadoop, Spark, and Pig. Understanding the basics of technologies such as Deep learning, Machine learning, and the like also can propel your career in this role.

Responsibilities

The responsibilities you have to shoulder as a data scientist include:

1. Manage, mine, and clean unstructured data to prepare it for practical use. 

2. Develop models that can operate on Big Data

3. Understand and interpret Big Data analysis

4. Take charge of the data team and help them towards their respective goals

5. Deliver results that have an  impact on business outcomes

Salary of data scientist

As a data scientist, you can earn as much as $137,000 a year.

Data Analyst 

A Data Analyst occupies an entry-level role in a data analytics team. In this role, you need to be adept at translating numeric data into a form that can be understood by everyone in an organization. Moreover, you need to have required proficiency in several areas, including programming languages such as python, tools such as excel, and fundamentals of data handling, reporting, and modelling.  With enough experience under your belt, you can gradually progress from a data analyst to assume the role of a data engineer and a data scientist.

The Required Skillsets

When we talk about the role of a data analyst, you should know that it is less technical. It is an entry-level role, and you need to have an understanding of tools such as SAS Miner, Microsoft Excel, SPSS, and SSAS. If you have a basic knowledge of Python, SQL, R, SAS, and JavaScript, it would be a plus point. 

Responsibilities

As a data analyst, you will have to assume specific responsibilities, including:

1. Collecting information from a database with the help of a query

2. Enable data processing and summarize results

3. Use basic algorithms in their work like logistic regression, linear regression and so on

4. Possess and display deep expertise in data munging, data visualization, exploratory data analysis and statistics

Salary of data analyst

Data analysts can expect an average salary of $67,000 per year, which is remarkable, considering that it is an entry-level role.

Data Engineers 

Data Engineers are the intermediary between data analysts and data scientists. As a data engineer, you will be responsible for the pairing and preparation of data for operational or analytical purposes. A lot of experience in the construction, development, and maintenance of data architecture will be demanded from you for this role. Usually, in this role, you will get to work on Big Data, compile reports on it, and send them to data scientists for analysis.

Related post: Data Science: Free Online Courses For 2023

The Required Skillsets

The role of a Data Engineer requires you to have a deep understanding of programming languages such as Java, SQL, SAS, Python, and the like. You should also be adept at handling frameworks such as Hadoop, MapReduce, Pig, Hive, Apache Spark, NoSQL, and Data Streaming, at naming a few.

Responsibilities

Your responsibilities in this role are:

1. Data Mining for getting insights from data

2. Conversion of erroneous data into a useable form for data analysis

3. Writing queries on data

4. Maintenance of the data design and architecture

5. Develop large data warehouses with the help of extra transform load (ETL)

Salary of a data engineer

Data engineers can have a salary upwards of $116,000 a year which is remarkable.

September 7, 2020 by Data Science

Highest Paying Jobs with an Economics Degree

What can you do with an economics degree? This is a question many prospective college students ask when considering this popular major. While there may be many potential answers to this question, some of the most lucrative ones are highlighted below. If you want to study economics and earn a handsome salary upon graduation, these are the career paths you should consider.

Job TitleMedian Salary (2018)*Job Growth (2018-2028)*
Economist$104,3408%
Budget Analyst$76,2204%
Top Executive$104,9806%
Personal Financial Advisor$88,8907%
Financial Manager$127,99016%
Source: *U.S. Bureau of Labor

1. Economist

With a bachelor’s degree in economics, you would be qualified for entry-level positions and pursuing a graduate degree in economics would open up several other options. Economist uses analytical and research skills to carry out studies regarding economic scenarios. They analyze industry trends to help organizations improve their performance.

They might work for organizations in a variety of industries, including business, finance, healthcare, education, the government, and more. The economist can also act as an expert witness in legal cases to assess economic damages, analyze intellectual property and antitrust violations, and address regulatory violations.  In 2018, economists made a median annual salary of $104,340.

2. Budget Analyst

A budget analyst is employed by an organization or business and is responsible for helping the company develop and manage an annual budget. They may work closely with other company executives and managers to understand the financial needs of each department, as well as the expenditures.

An economics bachelor’s degree would be adequate preparation for a career as a budget analyst, though certification may also be helpful for those working in the government. These professionals made a median salary of $76,220.

Related post: Economics Books For Economist.

3. Top Executive

Top executives work in several different capacities for companies and organizations, like operations managers, executive directors, and CEOs. While their responsibilities and job duties may vary depending on their specific role, top executives are often in charge of setting organizational goals, working with other company executives, analyzing sales and financial reports, and overseeing the daily operations of a company.

With an economics degree and relevant work experience, you would be qualified for a position as a top executive. In 2018, top executives made a median annual salary of $104,980 and there is the potential to earn even more depending on your role, as the top ten per cent of CEOs made over $208,000 in 2018.

4. Personal Financial Advisor

As a personal financial advisor, you will work closely with clients who are seeking financial advice concerning their investments and savings. You may help them set up savings funds for retirement or their children’s future education, manage their investment portfolios, and navigate tax season.

A degree in economics would provide an individual with a good foundation for a career as a personal financial advisor and certifications or licenses may provide an additional advantage. These advisors made a median annual salary of $88,890 in 2018, which is also much higher than the national median of $38,640 for all occupations.

5. Financial Manager

Financial managers play a key role in businesses and organizations, as they oversee all of the business’s financial activities and make important financial decisions. For example, a financial manager would be responsible for managing the finance department and all of its employees, producing financial reports and statements, making sure budgets are set and abided by, and reporting to top executives.

Financial managers typically have a bachelor’s degree in a relevant field, like economics, and significant work experience in a related role. In 2018, financial managers earned a median annual salary of $127,990.

September 3, 2020 by Economy

Advantages of using Data Analysis In Small Business

Data analysis isn’t just for corporations. Small businesses can benefit from big data technology, too. You can do it. Even though you operate a small business, you can take advantage of the power of big data analytics.

The value of knowing the customer is one of the biggest benefits of big data, but it is not the only one. Here are some advantages to use data analysis in your small business.

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Understanding how your customers think

10 Ways to Make Customers Fall in Love with Your Business

Thanks to data, small businesses can get a big picture of their customers how they think, why they buy, how they prefer to shop, why they switch, what they’ll buy next, and what factors lead them to recommend a company to others.

Companies can also better interact and engage with customers by analysing customer feedback in order to improve a product or service. Useful data sources include traditional in-house data (like sales data and customer service logs), social media, browser logs, text analytics, and large, public data sets.

Social media has become a particularly valuable source of data, making activities such as identifying niche markets and analysing customer feedback much easier and cheaper. Twitter, where almost all conversations are effectively held in public, is easier to mine than most platforms.

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A Proactive Approach to Your Small Business

Analytics, Chart, Drawing, Colors, Graph

It is easy to be reactive when running a small business, but a proactive approach is better, and potentially more profitable. Analyzing your existing data allows you to move from mere reaction to anticipating the needs of your customers.

Small business owners can use data gleaned from past orders to recommend new products and services to their customers. This proactive approach lowers costs, harnesses existing relationships, builds brands and grows profits over time.

Related post: Start A Business With No Money

Checking out the competition

Advantages of using Data Analysis In Small Business

In the past, understanding your competition was limited to industry gossip or looking around rivals’ websites or shops. Some might go as far as pretending to be customers in order to find out more about a competitor’s service or product. These days though, you hardly need to leave your desk to find out what the competition is up to; financial data is readily available, Google Trends can offer insights on the popularity of a brand or product, and social media analysis can illustrate the popularity and show what customers are saying.

Again, Twitter is a particularly transparent place to start. All the information you gather can be compared with your own brand; for example, does your competitor get more mentions on Twitter? How do their Twitter conversations with customers compare with yours?

Keep in mind that it’s also easy for your competitors to glean more information on your business than ever before. There’s no way around this, but you can stay one step ahead by keeping up-to-date on the latest big data technologies and uses.

Related post: COVID-19 Pandemic: Top Business Opportunities

A More Effective Online Presence

Advantages of using Data Analysis In Small Business

Whether your small business operates in the real world or just in cyberspace, a strong online presence is essential. Unfortunately, it is not always easy to allocate those online resources, and many small business owners struggle to build their brands online.

Small businesses also find it difficult to cut through the clutter and reach their intended demographics, but data analysis can make the process easier and more effective. By harnessing the power of data analytics, small business owners gain insight into everything from which keywords bring in the business to which products are the hottest sellers. By making online marketing more effective, data analytics can lower costs, enhance brand loyalty and boost profits.

Related post: Top 10 Data Science Books For Your Career

Improve Future Products

Advantages of using Data Analysis In Small Business

Even the most successful small business cannot afford to rest on its laurels or rely on its past successes. Even if you have been blessed with a blockbuster product, you need to be gearing up for your next act.

Data analytics can help you make your existing products better while designing additional products your customers will want to buy. By harnessing disparate sources of data from a variety of sources, quality analysis can uncover hidden issues with current products and provide clues for further improvements.

August 25, 2020 by Business

Complete Career Guide For A Data Scientist

In the fast-paced world of technology, the role of a data scientist has become increasingly crucial. As businesses rely on data-driven insights to make informed decisions, the demand for skilled data scientists continues to soar. If you aspire to embark on a career in data science, this comprehensive guide will walk you through the essential steps and considerations to build a successful path.

Introduction

The field of data science is a dynamic and interdisciplinary domain that combines expertise in statistics, mathematics, and computer science. As businesses harness the power of data to gain a competitive edge, the role of a data scientist has evolved into one of the most sought-after positions in the job market.

Understanding the Role of a Data Scientist

At its core, a data scientist is responsible for extracting meaningful insights from complex datasets. This involves employing statistical analysis, machine learning algorithms, and data visualization techniques. The role is not only about crunching numbers but also requires effective communication to convey findings to non-technical stakeholders.

Complete Career Guide For A Data Scientist
Complete Career Guide For A Data Scientist
Download (PDF)

Educational Background and Skills Required

To embark on a career in data science, a strong educational foundation is crucial. Most data scientists hold advanced degrees in fields such as computer science, statistics, or mathematics. Additionally, acquiring certifications in relevant technologies and methodologies enhances one’s credibility in the field.

Building a Strong Foundation in Programming

Proficiency in programming is a cornerstone skill for a data scientist. The ability to code allows professionals to manipulate and analyze data efficiently. Python, R, and SQL are among the preferred programming languages in the field, with each offering unique advantages for specific tasks.

Specializations in Data Science

Data science encompasses various specializations, including machine learning, data engineering, and business analytics. Aspiring data scientists should explore these branches to identify their interests and align their career goals accordingly. Each specialization presents distinct opportunities and challenges.

Gaining Practical Experience

While theoretical knowledge is essential, gaining practical experience is equally crucial. Engaging in real-world projects, and internships, and participating in open-source initiatives contribute significantly to skill development. Platforms like Kaggle provide a competitive environment for honing problem-solving skills.

Networking in the Data Science Community

Building a network within the data science community opens doors to valuable opportunities. Joining online forums, attending conferences, and networking events allows aspiring data scientists to connect with industry professionals, share insights, and stay abreast of the latest trends.

Crafting an Impressive Data Scientist Resume

A well-crafted resume is the first step in securing a data science position. Highlighting academic achievements, relevant projects, and acquired skills is essential. A strong resume showcases expertise and demonstrates the ability to apply knowledge in practical scenarios.

Preparing for Data Science Interviews

Data science interviews often include technical assessments and problem-solving challenges. Aspiring data scientists should prepare for common interview questions, showcasing their analytical and problem-solving abilities. Effective communication of methodologies and findings is equally important.

Salary Expectations and Job Market Trends

Salary expectations in data science vary based on factors such as experience, location, and industry. Analyzing current job market trends provides insights into the demand for specific skill sets. Staying informed about salary benchmarks ensures fair compensation negotiations.

Continuous Learning and Professional Development

Data science is a rapidly evolving field. Continuous learning is essential to stay updated on new technologies and methodologies. Online courses, workshops, and industry conferences provide opportunities for professional development.

Challenges Faced by Data Scientists

While a career in data science is rewarding, it comes with its challenges. Aspiring data scientists should be prepared to face obstacles such as handling massive datasets, overcoming algorithmic biases, and addressing ethical considerations in data analysis.

Diversity and Inclusion in Data Science

Diversity and inclusion are integral to fostering innovation in data science. Initiatives and programs promoting diversity create a more vibrant and creative work environment. Embracing diverse perspectives enhances problem-solving and contributes to a more robust data science community.

Future of Data Science

The future of data science holds exciting possibilities with emerging technologies such as artificial intelligence, blockchain, and quantum computing. As the field evolves, data scientists can anticipate new challenges and opportunities, making adaptability and continuous learning paramount.

Conclusion

Embarking on a career in data science is an exciting journey filled with opportunities for growth and innovation. By following this comprehensive career guide, aspiring data scientists can navigate the complexities of the field, build a strong foundation, and contribute meaningfully to the ever-expanding world of data.

Download: The Data Science Handbook

August 14, 2020 by Data Science

The Inventions That Changes The Modern Economy

Human inventions and technologies have shaped civilizations and transformed life on Earth.  As expectations and capabilities evolve, each new generation possesses its own set of innovative thinkers. Right from the invention of the wheel to the development of Mars rover, several inventions are revolutionary.

The past decade has seen a significant-tech boom and an increase in products featuring smart technology. Here are some of the most important and influential inventions that change the modern economy.

Related post: Economics Books To Study If You are Going To Be Economist.

1.Search Engine

Google, Www, Online Search, Search

Google isn’t the only search engine available, it certainly is the most popular. Since it’s creation the website has achieved things that were previously impossible, from SEO optimisation to seamless global collaboration. Search engines give us access to limitless information 24 hours a day. It completely changed how we consume news and find out about current events. Search engines have changed marketing forever. Whether its pay per click advertising campaigns or SEO optimisation, most businesses now promote themselves online and rely heavily on sites like Google to pull in results.

2. Smartphones

Smartphone, Technology, Mockup, Apps


Yes, phones existed before 2000. The age of the smartphone, however, is a whole different ball game. There have been many variations and the smartphone now is far more advanced than even 5 years ago. However, the role that modern-day smartphones play in our day to day lives is undeniable. Being able to access almost any type of information or service at the touch of a button must surely make the smartphone one of the very best inventions of all time.

3.Artificial Intelligence

Artificial Intelligence, Brain, Think


Sci-fi movies from older times feature talking robots and computers that can think for themselves. These things still feature in movies, admittedly, but artificial intelligence is becoming a real “thing”. In 2011, for instance, a computer system called IBM Watson competed on and won American quiz-show Jeopardy, beating two all-time champions in the process. Though we’re not at a stage where robots are uprising and taking over the world, the capability to develop full artificial intelligence can’t be too far off.

4. Blockchain

Bitcoin, Blockchain, Crypto

You’ve likely heard about it even if you don’t fully understand it. The simplest explanation of blockchain is that it is an incorruptible way to record transactions between parties – a shared digital ledger that parties can only add to and that is transparent to all members of a peer-to-peer network where the blockchain is logged and stored. The technology was first deployed in 2008 to create Bitcoin, the first decentralized cryptocurrency, but it has since been adopted by the financial sector and other industries for myriad uses, including money transfers, supply chain monitoring, and food safety.

5. 3D printing

The Inventions That Changes The Modern Economy

The earliest application of the layering method used by today’s 3D printers took place in the manufacture of topographical maps in the late 19th century, and 3D printing as we know it began in 1980. The convergence of cheaper manufacturing methods and open-source software, however, has led to a revolution of 3D printing in recent years. Today, the technology is being used in the production of everything from lower-cost car parts to bridges to less painful ballet slippers and it is even considered for artificial organs.

6. The Electric Car

Tesla, Tesla Model X, Charging


If you haven’t heard of Elon Musk and Tesla by now, we’re almost 100% certain you’re living in a media black hole. While his latest antics have been more geared towards space exploration, Tesla was an innovation of its own. Although electric cars had been invented previously, and subsequently, Tesla was the first to make them easily and readily available. Nissan and BMW have recently followed suit and produced their own commercially available electric cars.

7. High-density battery packs

lithium-ion cells

Tesla electric cars have received so much attention largely because of their batteries. The batteries, located underneath the passenger cabin, consist of thousands of high-density lithium-ion cells, each barely larger than a standard AA battery, nestled into a large, heavy battery pack that also offers Tesla electric cars a road-gripping low centre of gravity and structural support. The brainchild of Tesla co-founder J.B. Straubel, these battery modules pack more of a punch than standard (and cheaper) electric car batteries. These packs are also being used in residential, commercial, and grid-scale energy storage devices.

8. Facebook

Facebook logo


Facebook, introduced in 2004, wasn’t the first social media site to grace the internet but it was by far the most influential. Initially developed for Harvard students only, Facebook now has over 2 billions users. The ability to share your life with a multitude of contacts and use the internet to build lasting networks is one that is now ingrained into everyday life for both business and individuals.

9. YouTube

YouTube


YouTube arrived on our screens in 2005 just over a decade ago. To think that YouTube has changed the face of media consumption in such a short space is incredible. For everything from how-to videos, to comedy show catch-ups, viral videos and marketing campaigns, YouTube is to go-to whenever a video element is needed.

10. The Apple iPod

The Inventions That Changes The Modern Economy


Now it becomes I tunes. The iPod was the gadget that transformed how we listen to music. Gone were the days of walkmans and CD’s, and the ability to carry around so many songs in such a small device made it the must-have item of 2001.

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August 12, 2020 by Economy

How to use LinkedIn for your career progression?

Learning how to use LinkedIn for professional development is a great step toward improving your network, keeping in touch with colleagues, finding that perfect job and making new connections that offer exciting opportunities. LinkedIn is one of the fastest-growing and most valuable assets to access. In 2019 alone, LinkedIn had 645 million accounts, and 40% of those users actually logged on every single day.  With over 30 million companies now active on the platform, there were over 20 million open job postings in 2019 alone. 

Use LinkedIn for career progression:

1. Build an Outstanding Profile

LinkedIn provides a vast set of internal tools for building one’s profile. Make sure you fill out all areas and provide enough value in order to demonstrate your skills and qualifications in the best possible manner. Use a specific headline that corresponds with your core skill. Craft a genuine summary that outlines your top accomplishments and your main focus. Update your skills, the job experience (in details), communities you participate with and certificates that you hold. You can use a trustworthy photo which showcases your best self. Apply that same photo to your other online accounts so that people can connect the dots. Upload any SlideShare presentations or additional assets applicable to LinkedIn.

Remember first impressions matter, and not just in person.  Before ever meeting you, 41% of recruiters have admitted judging candidates by their photos. Your profile picture is your calling card on LinkedIn.

2. Use the app to your advantage.

When you are on the job hunt, find companies you want to work for, and make it a point to follow their accounts so you can stay aware of the company’s current happenings.  The “companies” tab will display updates, employees and job postings, all in one central location that will then make its way over to your news feed. Once you land an interview, you will already be well up to date on their latest product releases or corporate announcements, and this makes you stand out more than you realize

The app also contains a pulse tab, which allows you to see personalized articles and topics related to your industry and target audience. Dedicate a little time each morning to sift through the app. With your morning cup of coffee, spend 10 minutes finding articles worth sharing, or work towards building content around your industry (articles, blog posts, etc) so you can establish yourself as a thought-leader in your niche. 

3. Produce Regular Valuable Content

Out of the 645 million LinkedIn accounts, less than .5% are actually contributing new content.  This place is a goldmine for sharing content and being seen. Low competition means high view opportunities, a recipe for success.   If you find an article you liked within your realm of subject matter, read the comments section to find questions that other viewers may have posed as a way to search for and address gaps that weren’t covered on the topic. Tag other people who will get notified once mentioned – especially if you discuss more active LinkedIn members.

Recording daily educational videos (or every few days) will most likely receive more attention than standard textual posts. If you want to build your portfolio for work purposes, make sure your topics are closely related to your speciality. You want to end up with a complete profile that screams “professional” and is focused on what you do and are eager to do.

4. Connect with the right people

You met someone a few years back in college doesn’t necessarily mean you want to connect with them on this professional platform. Your level of reach depends largely on how social and engaged you are on the platform, not necessarily how many people are connected with you.  Start by connecting with existing professional and personal contacts in the industry you are working in, or want to move into.  If you meet a new coworker or connect with a professional during a networking event, seek out their account and send them a LinkedIn request that contains a short personalized message.

Join LinkedIn groups and interact with people. Comment on discussions, share topics on your own feed, mention other team members. Most people would be sending invitations every now and then and don’t be afraid to ask for a connection request after a couple of interactions.

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July 31, 2020 by Data Science

Best Statistical Analysis Software

Statistical software is a specialized computer program which helps you to collect, organize, analyze, interpret and statistically design data. There are two main statistical techniques which help in statistical data analysis: descriptive statistics and inferential statistics.

Descriptive statistics organize data from a sample using indexes. Inferential statistics draw a conclusion from data that is a random variant. Statistics are crucial for organizations. They provide factual data which is critical in detecting trends in the marketplace so that businesses can compare their performance against their competitors. These are the best statistical analysis software:

1. SPSS (IBM)

SPSS, (Statistical Package for the Social Sciences) is perhaps the most widely used statistical software package in human behaviour research. SPSS offers the ability to easily compile descriptive statistics, parametric and non-parametric analyses, as well as graphical depictions of results through the graphical user interface (GUI). It also includes the option to create scripts to automate analysis or to carry out more advanced statistical processing.

2.RStudio

The primary mission of RStudio is to build a sustainable open-source business that creates software for data science and statistical computing. You may have already heard of some of our work, such as the RStudio IDE, Rmark down, shiny, and many packages in the tidy verse. Our open-source projects are supported by our commercial products that help teams of R users work together effectively, share computing resources, and publish their results to decision-makers within the organization. 

3. Stata

Stata: Software for Statistics and Data Science

Stata puts hundreds of statistical tools at your fingertips. For data management, statistical analysis, and publication-quality graphics, Stata has you covered.

4. OriginPro

Origin is a user-friendly and easy-to-learn software application that provides data analysis and publication-quality graphing capabilities tailored to the needs of scientists and engineers. OriginPro offers extended analysis tools for Peak Fitting, Surface Fitting, Statistics, Signal Processing and Image Handling. Users can customize operations such as importing, graphing and analysis, all from the GUI. Graphs, analysis results and reports update automatically when data or parameters change. 

5. Microsoft Excel

microsoft excel

While not a cutting-edge solution for statistical analysis, MS Excel does offer a wide variety of tools for data visualization and simple statistics. It’s simple to generate summary metrics and customizable graphics and figures, making it a useful tool for many who want to see the basics of their data. As many individuals and companies both own and know how to use Excel, it also makes it an accessible option for those looking to get started with statistics.

Have you read this: Data Science: Free Online Courses For 2020

6. SAS Base

SAS BASE

SAS Base is a programming language software that provides a web-based programming interface; ready-to-use programs for data manipulation, information storage and retrieval, descriptive statistics and reporting; a centralized metadata repository; and a macro facility that reduces programming time and maintenance headaches.

7. MATLAB

Matlab

MatLab is an analytical platform and programming language that is widely used by engineers and scientists. As with R, the learning path is steep, and you will be required to create your own code at some point. A plentiful amount of toolboxes are also available to help answer your research questions (such as EEGLab for analysing EEG data). While MatLab can be difficult to use for novices, it offers a massive amount of flexibility in terms of what you want to do as long as you can code it.

8. Analyse-it

Analyse-it

Analyse-it is a statistical analysis software that includes hypothesis testing, model fitting, ANOVA, PCA, statistical process control (SPC) and quality improvement, and analytical and diagnostic method validation for laboratories to meet regulatory compliance.

9. GraphPad Prism

GraphPad Prism

GraphPad Prism is premium software primarily used within statistics related to biology but offers a range of capabilities that can be used across various fields. Similar to SPSS, scripting options are available to automate analyses, or carry out more complex statistical calculations, but the majority of the work can be completed through the GUI.

10. Minitab

Minitab

The Minitab software offers a range of both basic and fairly advanced statistical tools for data analysis. Similar to GraphPad Prism, commands can be executed through both the GUI and scripted commands, making it accessible to novices as well as users looking to carry out more complex analyses.

July 22, 2020 by Data Science