Advanced Certification in Python, Data Analysis, Data Visualization & Machine Learning

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Course Outline

Part-1 – Core Python

  1. Lesson 1

  2. Getting started with Python programming

        1. Overview
        2. Introductory Remarks about Python
        3. A Brief History of Python
        4. How python is differ from other languages
        5. Python Versions
        6. Installing Python and Environment Setup
        7. IDLE
        8. Getting Help
        9. How to execute Python program
        10. Writing your first Python program
        11. How to work on different Popular IDE’s    [Pycharm, Jupyter Notebook, Spyder etc.]

    Lesson 2

    Variables, Keywords and Operators

        1. Variables
        2. Memory mapping of variables
        3. Keywords in Python
        4. Comments in python
        5. Operators
          1. Arithmetic Operators
          2. Assignment Operators
          3. Comparison Operators
          4. Logical Operators
          5. Membership Operators
          6. Identity Operators
          7. Bitwise Operators
        6. Basics I/O and Type casting
        7. Getting user input

    Lesson 3

    Data types in Python

        1. Numbers
        2. Strings
        3. Lists
        4. Tuples
        5. Dictionary
        6. Sets

    Lesson 4

    Numbers and Strings

        1. Introduction to Python ‘Number’ & ‘string’ data types
        2. Properties of a string
        3. String built-in functions
        4. Programming with strings
        5. String formatting

    Lesson 5

    Lists and Tuples

        1. Introduction to Python ‘list’ data type
        2. Properties of a list
        3. List built-in functions
        4. Programming with lists
        5. List comprehension
        6. Introduction to Python ‘tuple’ data type
        7. Tuples as Read only lists

    Lesson 6

    Dictionary and Sets

        1. Introduction to Python ‘dictionary’ data type
        2. Creating a dictionary
        3. Dictionary built-in functions
        4. Introduction to Python ‘set’ data type
        5. Set and set properties
        6. Set built-in functions

    Lesson 7

    Decision making & Loops

        1. Introduction of Decision Making
        2. Control Flow and Syntax
        3. The if Statement
        4. The if…else Statement
        5. The if…elif…else Statement
        6. Nested if…else Statement
        7. The while Loop
        8. break and continue  Statement
        9. The for Loop
        10. Pass statement
        11. Exercise


    Lesson 8

    User defined Functions

        1. Introduction of functions
        2. Function definition and return
        3. Function call and reuse
        4. Function parameters
        5. Function recipe and docstring
        6. Scope of variables
        7. Recursive functions
        8. Lambda Functions / Anonymous Functions
        9. Map , Filter & Reduce functions

    Lesson 9

    Lessons and Packages

        1. Lessons
        2. Importing Lesson
        3. Standard Lesson – sys
        4. Standard Lesson – OS
        5. The dir Function
        6. Packages
        7. Exercise

    Lesson 10

    Regular expression

        1. Pattern matching
        2. Meta characters for making patterns
        3. re flags
        4. Use of match() , sub() , findall(), search(), split() methods

    Part-2 – Data Analysis

    Lesson 1

    GETTING STARTED WITH PYTHON LIBRARIES

        1. What is data analysis?
        2. Why python for data analysis?
        3. Essential Python Libraries Installation and setup
        4. Ipython
        5. Jupyter Notebook

    Lesson 2

    NUMPY ARRAYS

        1. Introduction to Numpy
        2. Numpy Arrays
        3. Numpy Data types
        4. Numpy Array Indexing
        5. Numpy  Mathematical Operations
        6. Indexing and slicing
        7. Manipulating array shapes
        8. Stacking arrays
        9. Sorting arrays
        10. Creating array views and copies
        11. I/O with NumPy
        12. Numpy Exercises

    Lesson 3

    WORKING WITH PANDAS

        1. Introduction to Pandas
        2. Data structure of pandas
        3. Pandas Series
        4. Pandas dataframes
        5. Data aggregation with Pandas
        6. DataFrames Concatenating and appending
        7. DataFrames Joining
        8. DataFrames Handling missing data
        9. Data Indexing and Selection
        10. Operating on data in pandas
        11. loc and iloc
        12. map,apply,apply_map
        13. group_by
        14. string methods
        15. Querying data in pandas
        16. Dealing with dates
        17. Reading and Writing to CSV files with pandas
        18. Reading and Writing to Excel with pandas
        19. Reading and Writing to SQL with pandas
        20. Reading and Writing to HTML files with pandas
        21. Pandas Exercises
  3. Part-3 – Data Visualization

    Lesson 1

    Matplotlib

        1. Introduction of Matplotlib
        2. Basic matplotlib plots
        3. Line Plots
        4. Bar Plots
        5. Pie Plots
        6. Scatter plots
        7. Histogram Plots
        8. Saving plots to file
        9. Plotting functions in matplotlib
        10. Matplotlib Exercises

    Lesson 2

    Seaborn

        1. Introduction of Seaborn
        2. Distribution Plots
        3. Categorical Plots
        4. Matrix Plots
        5. Bar Plots
        6. Box Plots
        7. Strip Plots
        8. Violin Plots
        9. Clustermap Plots
        10. Heatmaps Plots
        11. KDE Plots
        12. Regression Plots
        13. Style and Color
        14. Seaborn Exercise

    Lesson 3

    Plotly and Cufflinks

        1. Introduction to Plotly and Cufflinks
        2. Plotly and Cufflinks

    Lesson 4

    Geographical Plotting

        1. Introduction to Geographical Plotting
        2. Choropleth Maps – Part 1
        3. Choropleth Maps – Part 2
        4. Choropleth Exercises
        5. Projects using Analysis and Visualisation

     

    Part-4 – Machine Learning

    Lesson 1

    Introduction to Machine Learning

        1. What is Machine learing?
        2. Overview about scikit-learn  package
        3. Types of ML
        4. Basic steps of ML
        5. ML algorithms
        6. Machine learning examples

    Lesson 2

    Data Preprocessing

        1. Dealing with missing data
        2. Identifying missing values
        3. Imputing missing values
        4. Drop samples with missing values
        5. Handling with categorical data
        6. Nominal and Ordinal features
        7. Encoding class labels
        8. One hot encoding
        9. Split data into training and testing sets
        10. Feature scaling

    Lesson 3

    Machine Learning Classifiers

        1. K-Nearest Neighbors (KNN)
        2. Decision tree
        3. Random forest
        4. Support vector machines (SVM)
        5. Naive Bayes
        6. Logistic Regression
        7. Email Spam Filtering Project

    Lesson 4

    Regression Based Learning

        1. Simple Regression
        2. Multiple Regression
        3. Predicting house prices with Regression

     

    Lesson 5

    Clustering Based Learning

        1. Definition
        2. Types of clustering
        3. The k-means clustering algorithm

     

    Lesson 6

    Natural Language Processing

        1. Install nltk
        2. Tokenize words
        3. Tokenizing sentences
        4. Stop words with NLTK
        5. Stemming words with NLTK
        6. Twitter Sentiment analysis Project

    Lesson 7

    Working with OpenCV

      1. Installing opencv
      2. Reading  and writing images
      3. Applying image filters
      4. Writing text on images
      5. Image Manipulations
      6. Face detection Project
      7. Speech Recognition Project

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