Courses / Data Science

8 weeks

DURATION

online

MODE OF TRAINING

70

COURSES

3000+

GRADUATED STUDENTS

DATA SCIENCE Course Curriculum

  • What is data Science? – Introduction.
  • Importance of Data Science.
  • Demand for Data Science Professional.
  • Brief Introduction to Big data and Data Analytics.
  • Lifecycle of data science.
  • Tools and Technologies used in data Science.
  • Business Intelligence vs Data Science.
  • Role of a data scientist.
  • Python Programming Basics
    • Installing Jupyter Notebooks
    • Python Overview
    • Python 2.7 vs Python 3
    • Python Identifiers
    • Various Operators and Operators Precedence
    • Getting input from User, Comments, Multi line Comments.
  • Making Decisions and Loop Control
    • Simple if Statement
    • if-else Statement
    • if-elif Statement.
    • Introduction To while Loops.
    • Introduction to for Loops
    • Using continue and break
  • Python Data Types
    • Python Lists, Tuples, Dictionaries
    • Accessing Values
    • Basic Operations
    • Indexing, Slicing, and Matrixes
    • Built-in Functions & Methods
    • Exercises on List, Tuples and Dictionary
  • Functions and Modules
    • Introduction to Functions
    • Why Defining Functions
    • Calling Functions
    • Functions with Multiple Arguments.
    • Anonymous Functions – Lambda
    • Using Built-In Modules
    • User-Defined Modules
    • Module Namespaces
    • Iterators and Generators
  • File I/O And Exceptional Handling
    • Opening and Closing Files
    • open Function, file Object Attributes
    • close () Method, Read, write, seek. Exception Handling, the try-finally Clause
    • Raising an Exceptions, User-Defined Exceptions
    • Regular Expression- Search and Replace Regular Expression Modifiers
    • Regular Expression Patterns, re module
  • Numpy
    • Introduction to Numpy, Array Creation
    • Printing Arrays
    • Basic Operations- Indexing, Slicing and Iterating
    • Shape Manipulation – Changing shape, stacking and splitting of array Vector stacking
  • Pandas and Matplotlib And Seaborn
    • Introduction to Pandas Importing data into Python
    • Pandas Data Frames, Indexing Data Frames
    • Basic Operations with Data frame, Renaming Columns, Subletting and filtering a data frame.
    • Matplotlib – Introduction, plot (), Controlling Line Properties, Working with Multiple Figures, Histograms
    • Intro to Seaborn and Visualizing statistical relationships, Plotting with categorical data and Visualizing linear relationships.
  • Case Studies Using Numpy, Pandas
    • 3 Case Studies on Numpy, Pandas and Matplotlib
  • Descriptive Statistics
    • Describe or summarize a set of data
    • Measure of central tendency and measure of dispersion.
    • The mean, median, mode, Kurtosis and skewness Computing Standard deviation and Variance.
    • Types of distribution.
    • Class Handson:5 Point summary Boxplot Histogram and Bar Chart Exploratory analytics
  • Inferential Statistics
    • Introduction to inferential statistics
    • Different types of Sampling techniques
    • Central Limit Theorem
    • Point estimate and Interval estimate
    • Creating confidence interval for population parameter Characteristics of Z- distribution and T-Distribution Basics of Hypothesis Testing
    • Type of test and rejection region
    • Type of errors in Hypothesis resting, Type-l error and Type-ll errors
    • P-Value and Z-Score Method
    • T-Test, Analysis of variance (ANOVA) and Analysis of Co variance (ANCOVA)
    • Regression analysis in ANOVA
  • Hypothesis Testing
    • Hypothesis Testing
    • Basics of Hypothesis Testing
    • Type of test and Rejection Region
    • Type o errors-Type 1 Errors, Type 2 Errors
    • P value method, Z score Method
  • Introduction to Machine Learning
    • What is Machine Learning?
    • What is the Challenge?
    • Introduction to Supervised Learning
    • Unsupervised Learning
    • What is Reinforcement Learning?
  • Linear Regression
    • Introduction to Linear Regression
    • Linear Regression with Multiple Variables
    • Disadvantage of Linear Models
    • Interpretation of Model Outputs
    • Understanding Covariance and collinearity
    • Understanding Heteroscedasticity
    • Case Study – Application of Linear Regression for Housing Price Prediction
  • Logistic Regression
    • Introduction to Logistic Regression
    • Why Logistic Regression.
    • Introduce the notion of classification Cost function for logistic regression
    • Application of logistic regression to multi-class classification.
    • Confusion Matrix, Odd’s Ratio
    • ROC Curve Advantages and Disadvantages of Logistic Regression.
    • Case Study: To classify an email as spam or not spam using logistic Regression.
  • Decision Trees and Supervised Learning
    • Decision Tree – data set
    • How to build decision tree?
    • Understanding Kart Model Classification Rules-
    • Overfitting Problem Stopping Criteria and Pruning
    • How to Find final size of Trees?
    • Model A decision Tree.
    • Naive Bayes
    • Random Forests
    • Support Vector Machines
    • Interpretation of Model Outputs
  • Case Study
    • Business Case Study for Kart Model
    • Business Case Study for Random Forest
    • Business Case Study for SVM
  • Unsupervised Learning
    • Hierarchical Clustering
    • k-Means algorithm for clustering – groupings of unlabeled data points.
    • Principal Component Analysis (PCA)
    • Bagging and Boosting
    • Recommender System-collaborative filtering algorithm
    • Case Study– Recommendation Engine for movies
  • Introduction to Deep learning
  • Introduction to GIT HUB
  • Introduction to NLP, Computer vision
  • Introduction to Time series analysis
  • Introduction to Tableau
  • Resume preparation guidance

    Need Help? Chat with us