Step by Step – Learn Data Science using Python

 Learn Data Science using Python


If you are a beginner and are fascinated by the field of Data Science and would love to explore more about this amazing field, then below are the steps that you would need to explore step by step for an amazing life-changing career in Data Science. The below steps if followed properly with dedication can help you attain the Data Scientist position.

Below are the steps required to become a Data Scientist.


  • Learn various Packages/Libraries

    Get started with your Python skills and try to read the data and do some manipulations. There are numerous packages/libraries which would be required in your journey, for ex: NumPy, Pandas, Matplotlib, Sklearn, Statsmodels etc.

    Gradually you would start learning various packages/libraries on the go.

  • Mathematics, Linear Algebra & Statistics (Beginner to Intermediate level)

    Mathematics, Linear Algebra & Statistics are the foundation blocks for Data Science. Many of us already have the basic knowledge on these topics, so it’s time to have a bit more knowledge on these topics.

    For Linear Algebra: Click Here
    For Statistics: Click Here

    If you have no interest in all these, then Data Science is definitely not for you.

  • Identifying the business problem & Finalizing a solution

    This is one of the most important phase of a Data Scientist, he/she needs to understand the problem statement and come up with a solution. There’s no such guide for this particularly, but one can get to understand this with experience. Hence, it is suggested to go through multiple use cases across domains to get an idea of the solution and the approach.

  • Data Extraction, Cleaning & Pre-Processing

    After business problem identification, this is the second most important step where lot of data manipulation is required to convert the raw data to a certain format where ML/AI algorithms can be applied. All the NaN values are taken care, irrelevant features/columns are removed based on some analysis, lot of data pre processing is done.

  • Machine Learning/Deep Learning Algorithms (Intermediate to Advanced level)

    This step requires a deep understanding on various Machine Learning & Deep Learning algorithms.
    You can go through various free courses available on the web, here’s one of them: Click Here

  • Big Data (Beginner to Intermediate level)

    Here’s a free course on Hadoop: Click Here
    Here’s a free course on Scala/Spark: Click Here

    These courses are more than enough for a kick start, but understanding on various other tech stacks are required, as a data scientist’s role is not just identifying and implementing the algorithms, the main challenge is to push the models into production, hence knowledge on latest technologies is a must!!

    For a more detailed deep dive in Big Data Engineering , please refer to our free learning course on BIG DATA ENGINEERING
  • Data Visualization (Beginner to Intermediate level)

    Even though Visualization is just 5% of the entire data science project, but this is what helps you to sell your product.
    Data Visualization is done using various inbuilt Python libraries such as matplotlib, offline plots, plotly etc, but many Data Scientists also prefer using Tableau or Power BI for visualization. Hence beginner level of understanding on various BI tools are also required.

  • I hope this guide provides you enough information for a kick start.
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