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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

About Instructor

KudVenkat

Software Architect, Trainer, Author and Speaker in Pragim Technologies.

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