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Learn Python the Data Science Way

The first mistake we do while starting Python for Data Science is to start like a Programmer. Firstly, make sure about how are you going to use Python in your career?
If you want to be a Software Engineer – Try competitive coding to start with and then may be build a project or two
If you want to be a Web developer – Try Django
Similarly, if you want to be a Data Scientist then you need to learn it in the same way. We have divided the whole basics in part below, which will be followed by one detailed Machine Learning Algorithm and as many Hackathons as possible.

Python Learning path for data science aspirants

  • Basics
    • Installing Python & Python Environment
    • My first python program
    • Basic commands in Python
    • Data Types and Operations
    • If-then-else statement
    • Loops
    • Functions
    • Python packages
  • Python Libraries for Data handling and ML
    • Numpy
    • Pandas
    • Matplotlib
    • scikit-learn
  • Data Handling skills
    • Data importing
    • Working with datasets
    • Manipulating the datasets
    • Creating new variables
    • Exporting the datasets into external files
    • Data Merging
  • Basic statistics and reporting skills
    • Taking a random sample from data
    • Descriptive statistics
      • Central Tendency
      • Variance
      • Quartiles, Percentiles
    • Box Plots
    • Graphs
  • Data Cleaning and Data Preparation for Analysis
    • Raw Data – issues
    • Data Exploration
    • Data Validation
    • Data sanitization techniques

Spend 4-5 hours on each topic. We will put relevant article on each of these.

Keep Learning 🙂
The Data Monk

About TheDataMonkNewbie

I am the Co-Founder of The Data Monk. I have a total of 4+ years of analytics experience with 3+ years at Mu Sigma and 1 year at OYO. I am an active trader and a logically sarcastic idiot :)

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