Data Science with Python

DATA SCIENCE WITH PYTHON

Introduction to Data Science

• 1. What is Data Science?
• 2. Importance of data science
• 3. Demand for Data Science Professional
• 4. Data Science Life cycle
• 5. Tools and Technologies used in data science.
• 6. Roles and Responsibilities of a Data Scientist

COURSE 1: STATISTICS FOR DATASCIENCE

• 1. Module A: Introduction to Statistics
  • a. Statistics in Business
  • b. Types of Data
  • c. Data Measurement Scales
  • d. Fundamentals of Probability
• 2. Module B: Descriptive Statistics
  • a. Measures of central tendency (Mean, Median and Mode)
  • b. Measure of dispersion/spread (Variance and Standard Deviation)
  • c. Kurtosis and Skewness
  • d. Types of Probability Distributions
• 3. Module C: Inferential Statistics
  • a. What is inferential statistics
  • b. Different types of Sampling techniques
  • c. Central Limit Theorem
  • d. Point estimate and Interval estimate
  • e. Creating confidence interval for population parameter
  • f. Characteristics of Z-distribution and T-Distribution
• 4. Module D: Hypothesis Testing
  • a. Basics of Hypothesis Testing
  • b. Type of test and Rejection Region
  • c. Type of errors-Type 1 Error and Type 2 Errors
  • d. Parametric vs Non-Parametric Testing
  • e. ANOVA and Chi-Square testes
• 5. Module E: Correlation & Regression
  • a. Introduction to Regression
  • b. Type of Regression
  • c. Correlation
  • d. Weak and Strong Correlation

COURSE 2: PYTHON FOR DATA SCIENCE

• 1. Module A: Programming Basics - Python
  • a. Installing Jupiter Notebooks
  • b. Python Overview
  • c. Python various Operators and Operators Precedence
  • d. Getting input from user, comments, Multi line comments
• 2. Module B: Making Decisions and Loop - Python
  • a. Types of Operators
  • b. Data Types
  • c. Flow Controls (Loops)
  • d. Functions
  • e. List compressors
• 3. Module C: List,Tuples,Dictionaries– Python
  • a. Python Lists,Tuples,Dictionaries
  • b. Accessing Values
  • c. Basic Operations
  • d. Indexing, Slicing, and Matrixes
  • e. Built-in Functions & Methods
• 4. Module D: Functions And Modules – Python
  • a. Introduction To Functions – Why
  • b. Defining Functions
  • c. Calling Functions
  • d. Functions With Multiple Arguments.
  • e. Anonymous Functions - Lambda
• 5. Module F: Introduction of Essential Python Libraries for Data Science
  • a. Numpy
  • b. Pandas
  • c. Matplotlib
  • d. Scikit-learn
  • e. Seaborn
• 6. Module G: Numpy Package
  • a. Importing Numpy
  • b. Numpy overview
  • c. Numpy Array creation and basic operations
  • d. Indexing and Slicing
  • e. Iterating over array
  • f. Array manipulation
  • g. Numpy universal functions
  • h. Shape Manipulation
  • i. Stacking and Splitting Arrays
  • j. Indexing: Arrays of Indices, Boolean Arrays
• 7. Module H: Pandas Package
  • a. Importing Pandas
  • b. Pandas overview
  • c. Object Creation: Series Object , Data Frame Object
  • d. Handling the data and exporting the data
  • e. Pandas Sorting
  • f. Indexing, Selecting and filtering
• 8. Module I: Python Advanced: Data Mugging/Wrangling with Pandas
  • a. Handling Missing Data (Fillna, Dropna, Replace, Interpolate etc.,)
  • b. Group by Method
  • c. Merging, Joining and Concatenating Data Frames
  • d. Pivot Table
  • e. Reshaping the Data Frame using melt
  • f. Crosstab
• 9. Module J: Python Advanced: Visualization with Matplotlib and Seaborn
  • a. Introduction to Matplotlib
  • b. Creating basic chart : Line Chart, Bar Charts and Pie Charts
  • c. Plotting from Pandas object
  • d. Saving a plot
  • e. Multiple Plots
  • f. Plot Formatting : Custom Lines, Markers, Labels, Annotations, Colors
  • g. Statistical Plots with Seaborn (Distribution Plots, Categorical Plots, Matrix and regression plots)

COURSE 3: UNDERSTANDING AND IMPLEMENTING MACHINE LEARNING

• 1. Module A: Introduction to Machine Learning
  • a. What is Machine Learning
  • b. Applications of Machine Learning
  • c. Types of Machine Learning
  • d. Machine Learning Process
  • e. Python libraries suitable for Machine learning
• 2. Module B: Data Processing for Machine Learning
  • a. What is data preprocessing
  • b. Exploration of data (Uni-variate & Bi-variate analysis)
  • c. Outlier Detection and Treatment
  • d. Preprocess Data
    • i. Formatting
    • ii. Cleaning
    • iii. Sampling
  • e. Transform Data
• 3. Module C: Algorithms for Machine learning
  • a. Supervised Learning Algorithms
    • 1. Linear Regression
      • i. Concepts and Application
      • ii. Simple Linear Regression
      • iii. Multivariate Linear Regression
      • iv. Lasso Regression
      • v. Ridge Regression
    • 2. Logistic Regression – Concepts & Application
    • 3. kNN – Concepts & Application
    • 4. Decision Tree and random Forest – Concepts & Application
    • 5. Support Vector Machines – Concepts & Application
    • 6. Naïve Bayes – Concepts & Application
  • b. Unsupervised Learning
    • i. k Means Clustering
    • ii. Hierarchal Clustering
• 4. Module D: Dimensionality Reduction Techniques
  • a. PCA – Principal Component Analysis
  • b. LDA – Linear Discriminant Analysis
• 5. Module E: Other Topics
  • a. K-fold Cross Validation
  • b. Stratified Cross Validation
  • c. Boosting Techniques
    • i. Ada Boost
    • ii. XG Boost