IBM Certified Online Data Science Course with 100% Placements

INTRODUCTION

• What is Python?
• Why does Data Science require Python?
• Installation of Anaconda
• Understanding Jupyter Notebook
• Basic commands in Jupyter Notebook
• Understanding Python Syntax

Data Types and Data Structures

• Variables and Strings
• Lists, Sets, Tuples, and Dictionaries

Control Flow and Conditional Statements

• Conditional Operators, Arithmetic Operators, and Logical Operators
• If, Elif and Else Statements
• While Loops
• For Loops
• Nested Loops and List and Dictionary Comprehensions

Functions

• What is function and types of functions
• Code optimization and argument functions
• Scope
• Lambda Functions
• Map, Filter, and Reduce

File Handling

• Create, Read, Write files and Operations in File Handling
• Errors and Exception Handling

Class and Objects

• Create a class
• Create an object
• The __init__()
• Modifying Objects
• Object Methods
• Self
• Modify the Object Properties
• Delete Object
• Pass Statements

Numpy – NUMERICAL PYTHON

• Introduction to Array
• Creation and Printing of an array
• Basic Operations in Numpy
• Indexing
• Mathematical Functions of Numpy

2. Data Manipulation with Pandas

• Series and DataFrames
• Data Importing and Exporting through Excel, CSV Files
• Data Understanding Operations
• Indexing and slicing and More filtering with Conditional Slicing
• Group by, Pivot table, and Cross Tab
• Concatenating and Merging Joining
• Descriptive Statistics
• Removing Duplicates
• String Manipulation
• Missing Data Handling

DATA VISUALIZATION

Data Visualization using Matplotlib and Pandas

• Introduction to Matplotlib
• Basic Plotting
• Properties of plotting
• About Subplots
• Line plots
• Pie chart and Bar Graph
• Histograms
• Box and Violin Plots
• Scatterplot

Case Study on Exploratory Data Analysis (EDA) and Visualizations

• What is EDA?
• Uni – Variate Analysis
• Bi-Variate Analysis
• More on Seaborn based Plotting Including Pair Plots, Catplot, Heat Maps, Count plot along with matplotlib plots.

UNSTRUCTURED DATA PROCESSING

Regular Expressions

• Structured Data and Unstructured Data
• Literals and Meta Characters
• How to Regular Expressions using Pandas?
• Inbuilt Methods
• Pattern Matching

PROJECT ON WEB SCRAPING: DATA MINING and EXPLORATORY DATA ANALYSIS

• Data Mining (WEB – SCRAPING)
  This project starts completely from scratch which involves the collection of Raw Data from different sources and converting the unstructured data to a structured format to apply Machine Learning and NLP models. This project covers the main four steps of the Data Science Life Cycle which involves.

   

  • Data Collection
  • Data Mining
  • Data Preprocessing
  • Data Visualization
    Ex: Text, CSV, TSV, Excel Files, Matrices, Images

Data Types and Data Structures

• Statistics in Data science:
• What is Statistics?
• How is Statistics used in Data Science?
• Population and Sample
• Parameter and Statistic
• Variable and its types

Data Gathering Techniques

• Data types
• Data Collection Techniques
• Sampling Techniques:
• Convenience Sampling, Simple Random Sampling
• Stratified Sampling, Systematic Sampling, and Cluster Sampling

Descriptive Statistics

• What is Univariate and Bi Variate Analysis?
• Measures of Central Tendencies
• Measures of Dispersion
• Skewness and Kurtosis
• Box Plots and Outliers detection
• Covariance and Correlation

Probability Distribution

• Probability and Limitations
• Discrete Probability Distributions
• Bernoulli, Binomial Distribution, Poisson Distribution
• Continuous Probability Distributions
• Normal Distribution, Standard Normal Distribution

Inferential Statistics

• Sampling variability and Central Limit Theorem
• Confidence Intervals
• Hypothesis Testing
• Z-test, T-test
• Chi-Square Test
• F-Test and ANOVA

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Introduction to Databases

• Basics of SQL
  • DML, DDL, DCL, and Data Types
  • Common SQL commands using SELECT, FROM, and WHERE
  • Logical Operators in SQL
• SQL Joins
  • INNER and OUTER joins to combine data from multiple tables
  • RIGHT, LEFT joins to combine data from multiple tables
• Filtering and Sorting
  • Advanced filtering using IN, OR, and NOT
  • Sorting with GROUP BY and ORDER BY
• SQL Aggregations
  • Common Aggregations including COUNT, SUM, MIN, and MAX
  • CASE and DATE functions as well as work with NULL values
• Subqueries and Temp Tables
  • Subqueries to run multiple queries together
  • Temp tables to access a table with more than one query
• SQL Data Cleaning
  • Perform Data Cleaning using SQL

INTRODUCTION

• What Is Machine Learning?
• Supervised Versus Unsupervised Learning
• Regression Versus Classification Problems Assessing Model Accuracy

REGRESSION TECHNIQUES

Linear Regression

• Simple Linear Regression:
• Estimating the Coefficients
• Assessing the Coefficient Estimates
• R Squared and Adjusted R Squared
• MSE and RMSE

Multiple Linear Regression

• Estimating the Regression Coefficients
• OLS Assumptions
• Multicollinearity
• Feature Selection
• Gradient Descent

Evaluating the Metrics of Regression Techniques

• Homoscedasticity and Heteroscedasticity of error terms
• Residual Analysis
• Q-Q Plot
• Cook’s distance and Shapiro-Wilk Test
• Identifying the line of best fit
• Other Considerations in the Regression Model
• Qualitative Predictors
• Interaction Terms
• Non-linear Transformations of the Predictors

Polynomial Regression

• Why Polynomial Regression
• Creating polynomial linear regression
• Evaluating the metrics

Regularization Techniques

• Lasso Regularization
• Ridge Regularization
• ElasticNet Regularization
• Case Study on Linear, Multiple Linear Regression, Polynomial, Regression using Python

CAPSTONE PROJECT:  A project on a use case will challenge the Data Understanding, EDA, Data Processing, and above Regression Techniques.

CLASSIFICATION TECHNIQUES

Logistic regression

• An Overview of Classification
• Difference Between Regression and classification Models.
• Why Not Linear Regression?
• Logistic Regression:
• The Logistic Model
• Estimating the Regression Coefficients and Making Predictions
• Logit and Sigmoid functions
• Setting the threshold and understanding decision boundary
• Logistic Regression for >2 Response Classes
• Evaluation Metrics for Classification Models:
• Confusion Matrix
• Accuracy and Error rate
• TPR and FPR
• Precision and Recall, F1 Score
• AUC-ROC
• Kappa Score

Naive Bayes

• Principle of Naive Bayes Classifier
• Bayes Theorem
• Terminology in Naive Bayes
• Posterior probability
• Prior probability of class
• Likelihood
• Types of Naive Bayes Classifier
• Multinomial Naive Bayes
• Bernoulli Naive Bayes and Gaussian Naive Bayes

TREE BASED MODULES

Decision Trees

• Decision Trees (Rule-Based Learning):
• Basic Terminology in Decision Tree
• Root Node and Terminal Node
• Regression Trees and Classification Trees
• Trees Versus Linear Models
• Advantages and Disadvantages of Trees
• Gini Index
• Overfitting and Pruning
• Stopping Criteria
• Accuracy Estimation using Decision Trees

Case Study: A Case Study on Decision Tree using Python

• Resampling Methods:
• Cross-Validation
• The Validation Set Approach Leave-One-Out Cross-Validation
• K-Fold Cross-Validation
• Bias-Variance Trade-O for K-Fold Cross-Validation

Ensemble Methods in Tree-Based Models

• What is Ensemble Learning?
• What is Bootstrap Aggregation Classifiers and how does it work?

Random Forest

• What is it and how does it work?
• Variable selection using Random Forest

Boosting: AdaBoost, Gradient Boosting

• What is it and how does it work?
• Hyper parameter and Pro’s and Con’s

Case Study: Ensemble Methods – Random Forest Techniques using Python

DISTANCE BASED MODULES

K Nearest Neighbors

• K-Nearest Neighbor Algorithm
• Eager Vs Lazy learners
• How does the KNN algorithm work?
• How do you decide the number of neighbors in KNN?
• Curse of Dimensionality
• Pros and Cons of KNN
• How to improve KNN performance

Case Study: A Case Study on KNN using Python

Support Vector Machines

• The Maximal Margin Classifier
• HyperPlane
• Support Vector Classifiers and Support Vector Machines
• Hard and Soft Margin Classification
• Classification with Non-linear Decision Boundaries
• Kernel Trick
• Polynomial and Radial
• Tuning Hyper parameters for SVM
• Gamma, Cost, and Epsilon
• SVMs with More than Two Classes

Case Study: A Case Study on SVM using Python

CAPSTONE PROJECT: A project on a use case will challenge the Data Understanding, EDA, Data Processing, and above Classification Techniques.