How to Become a Good Data Analyst?
By: Krzysztof Surowiecki
Tags: Data Visualization, Data Science, Data Analysis, Python, Statistics

Tips for Aspiring Newbies That Want to Be Outstanding Data Analysts.

Data science is becoming more and more attractive as an industry of interest for many young people. Many programmers think of switching their careers to data science as they can use their skills there. If you consider data analyst career path, take a look at my advices and tips. I believe they can help you to start well.

Aspiring data scientist should agree to never stop learning, to create a habit of lifelong studying new things, new tools, and new approaches to data science. It’s a lifelong journey of constantly being a ‘young data science student’ in a way. You can never know everything. It takes a lot of work and motivation to study, learn and analyze the data the way that is needed.

Learning Is Based on 4 Abilities:

Programming

You have to know how to program so you can work effectively with data. I highly recommend Python, as it is perfect for extracting data. ‘Why?’ you ask.

Python is the programming language of choice for data science. It overtook R in recent years, and in 2018, 66% of surveyed data scientists said they use it on a daily basis, making Python the new number one tool for analysts. That’s why you should learn Python if you consider a career in data science.

Python also gives the great possibility to work with data because its ecosystem consists of many data-oriented libraries which are easy to learn and effective to use.

2. Statistics

You need to easily interpret data, describe data sets, and create prognosis. Understanding of statistics will give you the right perspective to focus on valuable insights, and will help you present those insights to others in a convincing way.

3. Business

You have to learn to program and count so that your applications have a business impact. Data science has little use, if it is not applicable. Thanks to right interpretation of data, some companies may decide to change directions or can make adjustments that will help their business to thrive. Make your knowledge and skills useful and extract the best insight from data, while always taking the big picture into consideration.

This is how you become a business partner that everyone needs nowadays.

4. Team

You need to present your conclusions in a way that is understandable by your team, because your work is to benefit the others. Teamwork in every industry is important, but teamwork in data science is a must-have.

In big companies, whole BI departments work together; one person is creating data sets, another extracts them, while the third one can visualize them for the board. Cooperation is necessary. Due to business needs, your work can vary from day-to-day tasks, and you need to be flexible and understand other people’s needs.

How Can You Acquire those Abilities?

Make a good use of the Internet — there are many great sources that can help you to become a great data scientist. Choose the sources that give you constant motivation to learn.

Python Is Easier than You Think!

You can benefit from excellent courses created by Dan Bader on realpython.com.

Sign up to a forum pythonistacafe.com and discuss Python with others, where you can ask questions and share your insights. It is good to check your knowledge with other people, see different perspectives and learn more as a result.

For example, you might consider signing up to Datacamp.com. It is a learning platform for aspiring data scientists. They have excellent programs available, such as ‘Data Scientist with Python’, as well as an interesting podcast presented by Hugo Bowne-Anderson. It can be your other way of learning data science, even while commuting to work or cleaning your apartment.

You can also start the #100dayscodechallenge with TalkPython, I highly recommend it myself. It motivates you using gamification techniques. It can be useful when you feel stuck or unmotivated.

Challenge yourself with solving programming tasks on codechalleng.es. It is a good way to practice your skills and check what you can do and what you need to learn a bit more. You can also check out my colleague’s article on Python string formatting.

Enrich your knowledge with data libraries such as pandas, numpy — they are perfect to explore and play with data. Check matplotlib and bokeh, as they are useful data visualization libraries that can create graphs for you like the ones seen in Excel or Google Sheets.

Statistics for Everyone

Check out courses for data scientists on Udemy (udemy.com), they sometimes have great deals for some of their courses. After that, practise, practise and practise — you get better with every step you take.

Visit 365datascience.com. Their blog posts regarding statistics explain a lot and cover most of the issues that every beginner needs to work through.

Look up Think Stats, it is an outstanding book (with a free PDF version) that introduces all essential concepts. You’ll find this book at the top of most data science book lists, and there is a reason for that.

Data Visualization Will Help You More than You Can Imagine!

Put on data visualization — d3.js is a perfect choice for it.

Get to know Tableau or Power BI and learn how to visualize data. Join Tableau community and create your visualizations every week for makeovermonday. In the beginning it will be difficult, don’t get discouraged by critics at first, but later on it will surely help you in looking at data sets in a different, more informed way. You can also get to know Tableau on playfairdata.tv.

Communication Is Key

And last but not least, practice communication skills with others everyday — instead of writing an email, go to your colleagues’ desk and share your idea or difficulty. Talk to people you do not know, explain complex issues to 5 year olds and be creative. It is crucial to understand data from all points of view — business, users, clients. Getting this all-around outlook will definitely assist you in becoming a better data scientist.

Practise and Learn Along

Your learning process will never stop, think about it as a lifelong journey. Experiment, be bold, and do not be afraid to ask for help! No one knows everything and that is excellent, because you can always learn more, meet new people and face different approaches. I hope you will benefit from my tips and be happy in pursuing the data scientist career path.

You can also check out recommended books by other data scientists, especially those that are not about data science chosen by Isaac Faber!

And if you have any other insights that can help any aspiring data scientist, please share them in the comments or notes!