Certificate in Python for Data Analytics

Module 1 – Introduction to Python Basics – 6 hours

• Introduction to Python (History & Usage)
• Data Types in Python (Int, Boolean, Strings)
• Maths in ALU (Division, Addition & Multiplication)
• User Input & String Functions (Len, Lower, Upper)
• Variables & their memory allocation
• Control Flows (if, elif, else)
• Conditional Loops (for, while & Nested If)
• Indexing & Slicing the Strings

Module 2 – Introduction to Functions & List Comprehensions – 4 hours

• What are Functions?
• Types of functions (built-in, user-defined, anonymous)
• Creating your own function (def)
• Local vs. global scope
• Lambda Function (Anonymous Function)
• Map, Filter & Reduce functions
• List comprehensions & Its structure & simple Example

Module 3 – Data Structures in Python – 4 hours

• Introduction to NumPy arrays (creation- np.array())
• 1-D, 2-D, 3-D Arrays & Use cases in Python
• Array attributes (shape, size, dtype, etc.)
• Built-in Functions for Array Creation (np.zeros(), np.ones(), np.arange(), np.random)
• Array operations and broadcasting (Addition, multiplication, etc.)
• Accessing elements in Arrays & Negative indexing
• Multidimensional array indexing
• Reshaping Arrays (reshape(), flatten(), ravel())
• Aggregation Functions – (sum(), mean(), std(), min(), max())
• Correlations and Covariance – (corrcoef(), cov())
• Random Number Generation (np.random.rand(), np.random.randint())

Module 4 – All about Numpy – Numerical Python – 6 hours

• Introduction to Pandas DataFrames and Series
• Read excel, csv & connect to sql (pd.read_csv)
• Inspecting DataFrames (.head(), .tail(), .info(), .describe(), .shape)
• DataFrame Attributes and Methods (.shape, .columns, .index, .dtypes, .values)
• Indexing with loc and iloc
• Making a conditions columns (if/ else or by lambda function)
• Filtering data with conditions (np.where)
• Combining multiple conditions
• Data Cleaning techniques – Fillna, isna, dropna, drop_duplicates()
• Data Transformation & Preparation (df.rename(), astype, pd.to_datetime())
• Returning a new DataFrame – Understanding the inplace parameter
• Data manipulation with Pandas Selecting columns, rows
• Grouping data with groupby()
• Aggregating data (sum, mean, count, etc.)
• Applying custom aggregation functions
• Concatenating & Merging DataFrames (.merge, .join, .concat())

Module 5 – Introduction to OOP

• Classes and Objects – Defining classes, Creating objects
• Encapsulation – Access modifiers: Public, Private, Protected
• Inheritance – Base and derived classes & Multiple inheritance
• Polymorphism – Method overriding
• Abstraction – Abstract classes (abc module)
• Real-world OOP examples (e.g., Banking system, E-commerce platform)

Module 6 – Introduction to Matplotlib & Seaborn – 4 hours

• What is Matplotlib?
• Importance of data visualization
• Plotting simple lines and points
• Customizing Plots – Adding titles, customizing axes and labels, Adding legends
• Basic plot functions (plot(), scatter(), bar(), Histograms, Heatmaps etc.)
• Box & whisker Plot (Stats Plot)
• Creating multiple plots in a single figure (plt.subplot() and plt.subplots())
• Overview of Seaborn & Plotly for Interactive visualizations
• distplot(), kdeplot(), rugplot()
• 3D Plots

Module 7 – Introduction to OOPs – 3 hours

• It’s time to showcase what you have learnt so far..
• EDA Exercise: Analyzing a Dataset
• NumPy Exercise: Array Manipulations
• Pandas Exercise: DataFrame Manipulations
• Matplotlib Exercise: Data Visualization