Data Science

Data Science Syllabus
MODULE 1: PYTHON FOR DATA SCIENCE

Python Core:
• Introduction of python and comparison with other programming languages
• Installation of Anaconda Distribution and another python IDE
• Python Objects, Number & Booleans, Strings, Container objects, Mutability of objects
• Operators – Arithmetic, Bitwise, comparison and Assignment operators, Operators Precedence, and associativity.
• Conditions (If else, if-elif-else)
• Loops (While, for)
• Break and Continue statements
• Range functions

String objects and collections
• String object basics
• String methods
• Splitting and Joining strings
• String format functions
• List object basics
• List methods
• List as Stack and Queues

List Comprehensions Tuples, Set, Dictionaries and Functions
• Tuples, Sets, Dictionary object basics, Dictionary.
• Object Methods, Dictionary View Objects, functions basics, Parameter passing, Iterators.
• Lambda functions
• Map, Reduce, Filter functions
• Working with files•Reading and writing files
• Buffered read and write.Other File methods

OOPS concepts and working with files.
• Creating classes and Objects, Constructor
• Inheritance, Multiple Inheritance
• Polymorphism
1.) Over Loading [Method over Loading, Constructer over Loading,Operator over loading]
2.) Over riding: [Method over riding, Constructer over riding]
• Interfaces PDBC
• Multi-Threading (start(),display(),sleep(), yield, join…

Modules, Exception Handling and Database Programming
• Using standard module
• Creating new modules
• Exceptions Handling with Try-except
• Creating, inserting, and retrieving table
• Updating and deleting the data

Python Projects
• Number Guessing
• Hangman
• Python Story Generator
• Calculator
• Tic-Tac-Toe

Database
• My SQL

Python NumPy

• NumPy – ND array Object
• NumPy – Data Types
• NumPy – Array Attributes
• NumPy – Array Creation Routines
• NumPy – Array from Existing Data
• Array from Numerical Ranges
• NumPy – Indexing & Slicing
• NumPy – Advanced Indexing
• NumPy – Broadcasting
• NumPy – Iterating Over Array
• NumPy – Array Manipulation
• NumPy – Binary Operators
• NumPy – String Functions
• NumPy – Mathematical Functions
• NumPy – Arithmetic Operations
• NumPy – Statistical Functions
• Sort, Search & Counting Functions
• NumPy – Byte Swapping
• NumPy – Copies & Views
• NumPy – Matrix Library
• NumPy – Linear Algebra

 DATA VISUALIZATION USING MATPLOTLIB

• Line plot
• Bar graph
• Pie chart
• Subplots
• Histogram

DATA VISUALIZATION USING SEABORN

• Distribution plot
• Kde plot
• Count plot
• Box plot
• Scatter plot
• Sub plots
• Lmplot
• Pair plots

Python pandas

• Python Pandas – Series
• Python Pandas – Data Frame
• Python Pandas – Panel
• Python Pandas – Basic Functionality
• Python Pandas – Reindexing
• Python Pandas – Iteration
• Python Pandas – Sorting
• Working with Text Data
• Options & Customization
• Indexing & Selecting Data
• Python Pandas – Window Functions
• Python Pandas – Date Functionality
• Python Pandas – Time delta
• Python Pandas – Categorical Data
• Python Pandas – Visualization
• Python Pandas – IO Tools
• Descriptive Statistics
• Statistical Functions

MODULE 2: STATISTICAL METHODS

• Descriptive Statistics
• Sample vs Population statistics
• Random Variables
• Probability distribution function
• Expected value.
• Binomial Distribution
• Normal Distributions
• Z-score
• Central limit Theorem
• Hypothesis testing
• Z-Stats vs T-stats
• Type 1 type 2 error
• Confidence interval
• Chi-Square test
• ANOVA test
• F-stats

MODULE 3: TABLEAU/ POWER BI

3.1. INTRODUCTION TO TABLEAU

• Tableau tools / POWER BI Tools
• Datatmes in Tableau
• Viewing data

3.2. CREATING PIVOT TABLE

3.3. DATA BLENDING

3.4. CROSS DATABASE JOINS

3.5. CALCULATIONS ON DATA

• Aggregate functions

3.6. DATA VISUALIZATION IN TABLEAU

• Symbol maps
• Bar chart
• Stacked bar chin
• Line chart
• Pareto chart
• Heat map
• Pie chart
• Scatter plot
• Area chart
• Dual Axis chart
• Histogram
• Bubble chart

3.7. DASHBOARD CREATION

MODULE 4: MACHINE LEARNING IN AI

4.1. EXPLORATORY DATA ANALYSIS (EDA)

4.2. OUTLIERS AND THEIR TREATMENT

4.3. SUPERVISED LEARNING VS UNSUPERVISED LEARNING

4.4. FEATURE EXTRACTION AND CONVERSION

• One hot encoding using dummy variables
• One hot encoding using One hot encoder

4.5. REGRESSION MODELS

• Simple Linear regression
• Multiple Linear regression
• Polynomial Linear regression
• Ridge regression
• Bias and Variance tradeoff
• Lasso regression
• Elasticnet regression

4.6. CLASSIFICATION MODELS

• Logistic regression
• Naive Bayes (Gaussian NB and Multinomial NB)
• KNN Classifier
• SVM
• Regularization
• Kernel Trick
• Decision Tree
• Random Forest
• Conusion Matrix
• Bootstrapping, Bagging and Boosting

4.7. UNSUPERVISED LEARNING

• K-Means Clustering
• Elbow technique

4.8. ASSOCIATION RULE LEARNING

• Apriori Algorithm

4.9. MODEL SELECTION

• Selecting appropriate model for our data

Top Machine Learning Algorithms You Should Know

• Linear Regression.
• Logistic Regression.
• Linear Discriminant Analysis.
• Classification and Regression Trees.
• Naive Bayes.
• K-Nearest Neighbors (KNN)
• Learning Vector Quantization (LVQ)
• Support Vector Machines (SVM)

MODULE 5: DEEP LEARNING IN AI
5.1. INTRODUCTION TO DEEP LEARNING
• Biological Neural Network
• Artificial Neural Network
• Perceptrons
• Layers of a Network
5.2. ACTIVATION FUNCTIONS
• Identity Function
• Binary step function or Threshold function
• Logistic function or Sigmoid function
• ReLU function
• Hyperbolic Tangent function
• Softmax function
5.3. CREATING NEURAL NETWORK IN PYTHON
• ANN
• ANN with Activation functions
5.4. TENSORFLOW AND KERAS
• Variables
• Constants
• Placeholders
• Graph / Tensor / Session
5.5. ANN IN TENSORFLOW AND KERAS
5.6. CONVOLUTIONAL NEURAL NETWORK (CNN)
5.7. RECURRENT NEURAL NETWORK (RNN)

MODULE 6: NATURAL LANGUAGE PROCESSING IN AI (NLP)
6.1. NLP CONCEPTS
• Tokenization
• Stemming
• Lemmatization
• Stop words
• POS
6.2. FEATURE EXTRACTION
• CountVectorizer
• Tf-idfVectorizer
6.3. TEXT CLASSIFICATION USING NLP