How To Start With Data Science Career?

How To Start With Data Science? There’s no doubt about it data science in high demand. As of 2020, the average data scientist in the US makes over $113,000 a year, and data scientists in San Francisco make over $140,000. Learn data science and you could find yourself working in this promising, well-compensated field.

Just thinking about the first step can leave you dazed and confused, especially if you lack previous experience in the field. With so many different data science careers to explore, you might find yourself wondering which is the right one for you and if you’ve got what it takes to fit the profile.

Wondering how to start with Data Science. Start with this!

Is Data Science for Me?

Well, we’ve all asked ourselves that question when we were at square one of our data science learning path. And we haven’t forgotten that every expert was once a beginner.

1. So, this data science career guide has a three-fold purpose:

2. Show you why data science opportunities are worth exploring;

3. Inform you about the different careers in data science and boost your efficiency in discovering suitable data science roles;

4. Give you the know-how you need to pursue your professional data science path

Figure out what you need to learn

Data science can be an overwhelming field. Many people will tell you that you can’t become a data scientist until you master the following: statistics, linear algebra, calculus, programming, databases, distributed computing, machine learning, visualization, experimental design, clustering, deep learning, natural language processing, and more. That’s simply not true.

So, what exactly is data science? It’s the process of asking interesting questions and then answering those questions using data. Generally speaking, the data science workflow looks like this:

1. Ask a question

2. Gather data that might help you to answer that question

3. Clean the data

4. Explore, analyze, and visualize the data

5. Build and evaluate a machine learning model

6. Communicate results

This workflow doesn’t necessarily require advanced mathematics, a mastery of deep learning, or many of the other skills listed above. But it does require knowledge of a programming language and the ability to work with data in that language. And although you need mathematical fluency to become really good at data science, you only need a basic understanding of mathematics to get started.

It’s true that the other specialized skills listed above may one day help you to solve data science problems. However, you don’t need to master all of those skills to begin your career in data science. 

Get comfortable with Python and R

Python and R are both great choices as programming languages for data science. R tends to be more popular in academia, and Python tends to be more popular in the industry, but both languages have a wealth of packages that support the data science workflow. I’ve taught data science in both languages and generally prefer Python.

You don’t need to learn both Python and R to get started. Instead, you should focus on learning one language and its ecosystem of data science packages. If you’ve chosen Python (my recommendation), you may want to consider installing the Anaconda distribution because it simplifies the process of package installation and management on Windows, OSX, and Linux.

You also don’t need to become a Python expert to move on. Instead, you should focus on mastering the following: data types, data structures, imports, functions, conditional statements, comparisons, loops, and comprehensions. Everything else can wait until later!

Learn data analysis, manipulation, and visualization with pandas

For working with data in Python, you should learn how to use the panda’s library. pandas provide a high-performance data structure (called a “DataFrame”) that is suitable for tabular data with columns of different types, similar to an Excel spreadsheet or SQL table. It includes tools for reading and writing data, handling missing data, filtering data, cleaning messy data, merging datasets, visualizing data, and so much more. In short, learning pandas will significantly increase your efficiency when working with data.

However, pandas include an overwhelming amount of functionality, and (arguably) provides too many ways to accomplish the same task. Those characteristics can make it challenging to learn pandas and to discover best practices.

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Focus on practical applications and not just theory

While undergoing courses and training, you should focus on the practical applications of things you are learning. This would help you not only understand the concept but also give you a deeper sense on how it would be applied in reality.

A few tips you should do when following a course:

1. Make sure you do all the exercises and assignments to understand the applications.

2. Work on a few open data sets and apply your learning. Even if you don’t understand the math behind a technique initially, understand the assumptions, what it does and how to interpret the results. You can always develop a deeper understanding at a later stage.

3. Take a look at the solutions by people who have worked in the field. They would be able to pinpoint you with the right approach faster.

Keep learning and practicing

Here is my best advice for improving your data science skills: Find “the thing” that motivates you to practice what you learned and to learn more, and then do that thing. That could be personal data science projects, Kaggle competitions, online courses, reading books, reading blogs, attending meetups or conferences, or something else! Your data science journey has only begun! There is so much to learn in the field of data science that it would take more than a lifetime to master. Just remember: You don’t have to master it all to launch your data science career, you just have to get started!

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