DataScience Training in Bangalore

Course Name : DataScience Foundation

Course Contents

• Module 1 : Foundation for Data Science
  • Introduction to Data Science
  • Analytics Landscape
  • Life Cycle of a Data Science Projects
  • Data Science Tools & Technologies
• Module 2 : Probability & Statistics
  • Measures of Central Tendency
  • Measures of Dispersion
  • Descriptive Statistics
  • Probability Basics
  • Marginal Probability
  • Bayes Theorem
  • Probability Distributions
  • Hypothesis Testing
• Module 3: Basics of Python
  • Install Anaconda
  • Data Types & Variables
  • String & Regular Expressions
• Module 4: : Python Built-in Data Structures
  • Python list
  • Python dictionaries
  • Python set
  • Python tuple
  • Comprehensions
• Module 5: Control & Loop Statements in Python
  • For Loop & While Loop
  • Break Statement & Next Statements
  • Repeat Statement
  • if, if…else Statements
  • Switch Statement
• Module 6: Functions & Classes in Python
  • Writing your own functions (UDF)
  • Calling Python Functions
  • Functions with Arguments
  • Calling Python Functions by passing Arguments
  • Lambda Functions
  • Classes & Objects
• Module 7: Analyze Data using Pandas
  • Clean & Prepare Datasets
  • Manipulate DataFrame
  • Summarize Data
  • Churn Insights from Data
• Module 8: Introduction to Machine Learning
  • What is Machine Learning
  • Machine Learning Process
  • Types of Machine Learning
• Module 9: Visualize Data
  • Charts using Matplotlib
  • Charts using Seaborn
  • Charts using ggplot
• Module 10: Advanced Statistics & Predictive Modeling
  • ANOVA
  • Linear Regression (OLS)
  • Case Study: Linear Regression
  • Principal Component Analysis
  • Factor Analysis
  • Case Study: PCA/FA
  • Logistic Regression (MLE)
  • Case Study: Logistic Regression
  • K-Nearest Neighbor Algorithm
  • Case Study: K-Nearest Neighbor Algorithm
  • Decision Tree
  • Case Study: Decision Tree
• Module 11: Time Series Forecasting
  • Understand Time Series Data
  • Visualizing TIme Series Components
  • Exponential Smoothing
  • Holt's Model
  • Holt-Winter's Model
  • ARIMA
  • Case Study: Time Series Modeling on Stock Price
• Module 12: Introduction to Machine Learning
  • What is Machine Learning?
  • Supervised Learning & Unsupervised Learning
  • Using Scikit-learn
  • Scikit-learn classes
  • Case Study: Machine Learning Algorithm