Thread by @niketkedia

⭐Continuing the #DataScience learning path series and next skill is Python
⭐There always been a debate around R v/s Python
⭐Step by step learning path for Python for data analysis

1⃣ Intro to Python -

⭐Installing and Setting up environment
⭐Install Anaconda and Python
⭐Launch a Jupyter Notebook
⭐Variables and Operators
⭐Booleans and Comparisons
⭐expressions and statements

2⃣ Basics of Python -

⭐Functions, Modules and strings
⭐Lists, Tuples, Sets, and Dictionaries
⭐Installing a Package (Pandas, Scipy, NumPy,Plotly etc.)

3⃣Data cleaning and manipulation using Pandas, NumPy, Scipy -

⭐Creating, Reading and Writing Data
⭐Indexing, Selecting & Assigning
⭐Summary Functions and Maps
⭐Grouping and Sorting
⭐Data Types and Missing Values

3⃣Data cleaning and manipulation using Pandas, NumPy, Scipy -

⭐Renaming and Combining
⭐Handling Missing Values
⭐Scaling and Normalization
⭐Parsing Dates
⭐Character Encodings
⭐Inconsistent Data Entry

4⃣Data Visualization -

⭐Seaborn, Matlplotlib, plotly
⭐Line, Bar, Heatmaps, Scatterplots
⭐Histograms, Boxplots
⭐Maps
⭐Plot styles and custom styling

5⃣Machine Learning using Scikit-Learn -

⭐Linear Regression
⭐Logistic Regression
⭐Clustering
⭐Decision Trees, Support vector machines and Random Forest
⭐Intro to text analytics(natural language processing)

📄Summary:
1⃣ Intro to Python
2⃣ Basics of python
3⃣Data cleaning and manipulation using Pandas, NumPy, Scipy
4⃣Data Visualization
5⃣Machine Learning using Scikit-Learn

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