Data Analyst Development — Resources Backpack

We know that learning something new might be overwhelming. Now, with all the information floating on the internet things get messy, you don’t know where to start and most of the time you end up quitting. We’ve been there, so this article is trying to help you get a clearer picture on two things:
  1. What paths could you follow if you want to work in the Business Intelligence field?
  2. What are the best resources available for Data Analysts rookies?

To answer the first question, because we don’t want to reinvent the wheel, we invite you to read an article that better explains 9 career paths in data science:

  1. The Product Research Data Scientist
Data digital flow
  1. The Analytical Data Scientist
  2. The Analytics Data Scientist
  3. The Data Science Engineer
  4. The Data Engineer or Data Warehouse Engineer
  5. The Quantitative Analyst
  6. The Business Data Scientist
  7. The Executive Data Scientist
  8. Other Hybrid Data Scientists

Here, in eMAG, we have teams made of Analytics Data Scientist, Data Science Engineers, Data Warehouse Engineers or Business Data Scientists. If you’ll join our Data Analyst Development program you’ll get a glimpse of what everyone does and you’ll start as, BIG surprise, a Data Analyst.

Going back to the second question, we’ve compiled for you some of the best resources we’ve found on the internet related to our field, that we consider a preview of what you’ll learn in the program.

1. Based on this article, the best course for Intro to Data Science is this one.

This course will give you a full overview of the Data Science journey. Upon completing it, you will know:

  • How to clean and prepare your data for analysis;
  • How to perform basic visualization of your data;
  • How to model your data;
  • How to curve-fit your data;
  • Finally, how to present your findings and wow the audience.

2. Since the first one needs some cash, here’s a free course.

What you will learn?

  • Explore the data science process;
  • Probability and statistics in data science;
  • Data exploration and visualization;
  • Data ingestion, cleansing, and transformation;
  • Introduction to machine learning;
  • The hands-on elements of this course leverage a combination of R, Python, and Microsoft Azure Machine Learning.

3. Here are some introductory courses for each of our 4 main data roles presented above as an answer to the first question:

Business Intelligence: from big tables to beautiful dashboard (The Visualizer / Business Enabler)
Tools: QlikView, SQL, Google BigQuery
Learn: QlikView —

Big Data: from chaotic data to structured information (The Organizer)
Tools: Hadoop, Spark, Apache Cassandra
Learn: Hadoop MapReduce & HDFS —

DWH: from 100 tables to 1 big table (The Collector)
Tools: T-SQL, SQL Server Integration Services, MySQL
Learn: T-SQL —

Data Science: from big tables to actionable insights (The Insight Generator)
Tools: R, SQL, Machine Learning Algorithms
Learn: R —

4. Other learning resources

Best blog (Recommended by our colleague, Andrei)


Interactive platforms

  1. — wider range of skills covered (expand & go in depth on certain skills)
  2. — focus on the essentials (get it done approach)

Guys to follow

  1. — Tyler Byers
  2. — Jacob Brewer
  3. — Andrew Ng

Know more learning resources? Add a comment below!

Apart from the theoretical part of learning, we promise you the social and practical side of things. Working with our teams on real issues that affect our business. If we’ve captured your attention, we’re waiting for your application for our Data Analysis Development program.

*Written by Lavinia & Andrei

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