Data ScienceGeneral

How can I become a Data Scientist ?

A Data Analyst is someone who works with huge volumes of raw data and then analyses and predicts future outcomes. Data Science is becoming the talk of the town. Once known as the sexiest job, it is now one of the most profitable and fastest-growing industries in the world. Many University final year graduates as well working professionals are looking out for opportunities to opt for Data Science as their career path.

Here is a small idea of what Data Scientists do. So basically, The main work of the Data Scientists is to analyze the existing data using advanced learning methods and also for predicting the likelihood of a similar event in the future.

There are many paths to this data science career, and so for those thinking about what to study to become a data scientist, you have a a lot of options. Now let’s come to the main point, “How can I become a data scientist?

1. Reinforce your mathematical skills and Statistical data science.

Learning resources: 

Khan Academy courses for data science statistics.

  • probability
  • Linear algebra.
  • Matrix
  • Regression.
  • Correlational calculus.
  • Coordinate geometry.
  • ANOVA testing, etc.

2. Programming for data science Learn and gain proficiency in SQL

Programming is important for all roles. You can start with python programming, then gradually head into the R. Besides, you need to learn the basics of database management language too. Initially you can focus on SQL and MySQL.

Learning resources:

  • Codecademy
  • 365datascience
  • Datacamp
  • Udemy
  • Udacity
  • W3 School

3. Start reading blogs on data science

Keep yourself updated. Keep reading research papers and article and re-research the sections that you don’t understand.

  • This site is like the hub of all data science, ML and AI related posts – KDNuggets
  • Analytics Vidhya (See here)
  • Data Science 101

4. Start some serious Data Science Machine Learning

  • Introduction to Data Analytics at Udacity.
  • Machine learning models
  • ML by Andrew Ng at Stanford
  • Machine learning algorithm types.
  • Algorithms for classification
  • Algorithms for regressions.
  • Introduction to Deep learning at Udacity
  • Application of R and python for ML model designing and algorithm creation, etc.

Learning resources:

  • Machine learning nano degree program by Udacity
  • Deep learning specialisation by Andrew Ng in Coursera
  • Machine learning courses by EdX.
  • Google machine learning crash course.

5. Learn Data processing, Data Modeling, Data Analysis and Data visualisation

  • Keras(visualisation)
  • Numpy(python library)
  • Seaborn (visualisation)
  • MongoDB (database management)
  • Matplotlib (python library)
  • Pandas (python library)
  • ggplot2 (data visulization – R)
  • OpenCV (data analysis)
  • Power BI – Tableau(data analysis), etc. There are many more packages you can explore.

Good Luck Guys!!!

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