Data Science Roadmap | Complete Data Science Course For Free

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Part 1

Module 1

Variables, Data Types & Operators


Module 2

Conditionals and Loops

  • Conditional Statements 
  • If, elif , else statements and Nested if 
  • Loops 
  • For loop and range function 
  • While loop 
  • break and continue statements
  • Control flow statements 
  • Conditional statements and loops


Module 3

Strings Data Type

  • Strings 
  • Indexing on Strings 
  • Slicing on Strings 
  • String Methods - upper, lower, title, capitalize, etc.
  • Strings Methods

Module 4

List and its methods

  • What is list? 
  • Creating empty list 
  • Iterating in a list using for and while loop 
  • Indexing and slicing in list
  • List Methods - append, extend, clear, count, pop 
  • List Methods - index, insert, remove, sort, reverse 
  • List Comprehension


Module 5

Set and its methods

  • What is Set? 
  • Creating empty set and Unordered Set
  • Set Methods - add, union, update, difference, difference_update 
  • Set Methods - intersection, intersection _update, clear, pop, remove

Module 6

Tuple and Dictionary

  • What is Tuple? 
  • Creating empty tuple 
  • Immutable and ordered Tuple 
  • Iterating in a tuple 
  • Tuple Methods - index, count 
  • What is Dictionary?
  • Creating empty dict 
  • Keys and values in a dict 
  • Iterating in dict 
  • Dictionary Methods - get, keys, values, items, clear, etc.

Module 7

Functions

  • Different types of functions 
  • User defined Functions 
  • Creating functions with and without arguments 
  • Positional and default arguments 
  • Using return keyword in functions
  • Recursive Functions 
  • Args and kwargs 
  • Scope of variables - local and global 
  • Anonymous Functions – lambda

Module 8

Modules and Inbuilt Functions

  • Importing modules 
  • Using modules I - math, random 
  • Using modules II - itertools, collections
  • Inbuilt Functions I - map, reduce, filter 
  • Inbuilt Functions II - enumerate, eval, zip

Module 9

File Handling

  • Working with files 
  • Opening and closing a file 
  • Modes of opening a file 
  • Reading, writing and appending to a file 
  • Pickle File Handling

Module 10

Exception Handling

  • What is Exception? 
  • Understanding try-except-else block of code
  • What is Exception? 
  • Understanding try-except-else block of code 
  • Types of exceptions I - ZeroDivisionError, TypeError, NameError 
  • Types of exceptions II - ValueError, IndexError 
  • Handling multiple Exceptions

Module 11

Object Oriented Programming

  • Understanding class and objects 
  • Creating a class in Python 
  • Understanding constructor
  • Difference between a constructor and a method 
  • Getter and Setter methods 
  • Understanding Inheritance 
  • super method 
  • Polymorphism and method overriding 
  • Public private and protected variables 
  • Encapsulation

Module 12

Database connectivity with Python

  • Installing SQL Server/ MySQL 
  • Understanding fundamental SQL 
  • Creating, using and deleting database
  • Creating, inserting and updating a table in SQL 
  • Connecting Python with database 
  • Creating cursors

Module 13

Web Scraping

  • Installing BeautifulSoup and requests
  • Extracting URLs from a web page 
  • Scraping text data from a web page
  • Crawling multiple pages and scraping data from each of them 
  • Saving scraped data to a csv file 
  • Project - Scraping a website with BeautifulSoup

Part 2

Module 14

Introduction to Data Science

  • Overview of Data Science
  • Understanding the varied applications of Data Science
  • Different sectors using Data Science


Module 15

Statistics

  • Understanding the definition of Statistics?
  • Understanding data, sample and population
  • Types of data - Qualitative and Quantitative
  • Descriptive Statistics
  • Uni-variate Data Analysis - Measure of Central Tendency
  • Mean, Median and Mode
  • Uni-variate Data Analysis - Measure of Dispersion
  • Range, Variance, Standard Deviation
  • Bi-variate Data Analysis – Covariance and Correlation
  • Inferential Statistics
  • Central Limit Theorem
  • Random Variable
  • Probability Distribution Functions
  • Normal Distribution
  • Binomial and Poisson Distributions
  • Skewness
  • What is Hypothesis Testing?
  • Null and Alternate Hypothesis
  • P-value, Level of significance
  • Confidence Level and Confidence Interval
  • One Sample Z-test
  • Student’s T-test
  • Chi Square Test 

Module 16

NumPy

  • Introduction to Numpy 
  • Numpy Simple Array Creation using array and arrange method 
  • Numpy Custom Array Creation using zeros, ones, linspace etc
  • Reshaping numpy arrays
  • Generating numpy arrays with random values
  • Numpy 1D, 2D and 3D Indexing & Slicing
  • Numpy array operations - adding scalar value, row and column wise addition, adding array of same dimension
  • Iterating over numpy array
  • Numpy Array operations I - sum, max, min, argmax, argmin
  • Numpy Array operations II - sort, where, extract
  • Numpy Array operations III - insert, append, delete
  • Matrix related operations on numpy array
  • Matrix multiplication, transpose, determinant
  • Finding inverse, trace , flatten
  • Solving linear equations using numpy

Module 17

Pandas

  • Introduction to Pandas
  • Understanding Series in pandas
  • Creating Series using - numpy array, list, tuple, from a .csv/excel file
  • Series methods - mean, sum, count etc
  • Series indexing and slicing using - iloc and loc
  • Reading a .csv, .excel files using pandas - read_csv, read_excel
  • Understanding DataFrame in pandas
  • Creating DataFrame using - numpy array, list, tuple, from a .csv/ex cel file
  • Head, tail and sample methods for DataFrame
  • DataFrame indexing and slicing using - iloc and loc
  • Accessing column values from a DataFrame
  • Set DataFrame index, sort index and values
  • DataFrame query
  • Find unique values for a column in DataFrame
  • Groupby method
  • Data wrangling methods I - merge, append, concat
  • Data wrangling methods II - map, apply, applymap
  • Data cleansing I - rename columns, rearrange columns
  • Data cleansing II - remove null values, fill null values
  • Data cleansing III - drop rows, drop columns
  • Handling datetime in Pandas
  • Pivot table

Module 18

Matplotlib

  • Introduction to Matplotlib visualization
  • Bar Chart
  • Line Chart 
  • Scatter Chart 
  • Pie Chart 
  • Histogram 
  • Boxplot 
  • Subplots
  • stem plot 
  • stack plot 
  • step plot 
  • fill between 
  • savefig 
  • axis & text

Module 19

Seaborn

  • Introduction to Seaborn visualization 
  • Count plot 
  • Boxplot 
  • Violin plot 
  • Pair plot 
  • Heatmap 
  • Scatterplo
  • line plot 
  • bar plot 
  • histogram plot 
  • strip plot 
  • factor plot 
  • cat plot 
  • styling plot 
  • multiple plot ( Facet - Grid )

Module 20

EDA ( Exploratory Data Analysis )

  • Exploratory Data Analysis Overview 
  • Project - Coffee dataset EDA
  • Project - Bank dataset EDA

Part 3

Module 21

Machine Learnings

  • Overview of Data Science
  • Understanding the varied applications of Data Science
  • Different sectors using Data Science


Module 22

Feature Engineering

  • Data Cleaning - Removing and filling null values
  • Feature Selection - Correlation and Heatmap
  • Feature Selection - Backward Elimination & Forward Elimination
  • Methods of Encoding Categorical variables - One Hot Encoding & Dummy Variables, Label Encoding, Ordinal Encoding
  • Removing Outliers
  • Standardization and Scaling
  • Train Test Split in Data Set


Part 3 (A)

Module 23

Regression Analysis

  • Understanding the working and equation of Regression Analysis
  • Regression metrics - R2-score, MAE, MSE, RMSE, Adjusted R Squared 
  • Implementation of Simple & Multiple Linear Regression
  • Implementation of Ordinary Least Square(OLS) & Regularization 
  • Project - Heating and Cooling Load Prediction


Module 24

Classification Analysis

  • Understanding the working of Classification Analysis 
  • Implementation of Logistic Regression Understanding Confusion Matrix 
  • Classification Metrics - Accuracy, Precision, Recall, F1-Score, auc and roc curve
  • handling imbalanced datasets 
  • Bias Variance, Underfitting and Overfitting 
  • Project - Diabetic patient Classification

Module 25

Naive Bayes Algorithm

  • Understanding the working of Naive Bayes 
  • Implementation of Naive Bayes Classification 
  • Project - News Classification

Module 26

K-Nearest Neighbor (KNN) Algorithm

  • Understanding the working of KNN Classification & Regression 
  • Algorithm of KNN & Implementation of KNN 
  • Project - Social Network Ads Classification

Module 27

Tree based Models

  • Understanding the working of Decision Tree 
  • Understanding Gini and Entropy criterion
  • Implementation of Decision Tree Classification & Regression 
  • Project - Iris Flower Classification 

Module 28

Support Vector Machine ( SVM ) Algorithm

  • Understanding the working of Random Forest Classification & Regression 
  • Implementation of Random Forest Classification & Regression 
  • Difference between Bagging and Boosting

Module 29

Ensemble Learning

  • Understanding the working of Random Forest Classification & Regression 
  • Implementation of Random Forest Classification & Regression 
  • Difference between Bagging and Boosting
  • Understanding working of AdaBoost & XGBoost 
  • Implementation of AdaBoost & XGBoost

Module 30

Gradient descent, Cross-Validation & Hyperparameter tuning

  • Understanding the working of Gradient descent 
  • Type of Gradient descent 
  • Understanding of Batch, Stochastic& mini batch Gradient Descent 
  • Understanding the working of Cross-Validation
  • Types of Cross Validation ( Train & Test , K- Fold, Stratified k-fold etc.) 
  • Understanding the working of Model Parameter, Hyperparameter and Hyperparameter tuning 
  • Hyperparameter tuning using GridSearchCV & RandomizedSearchCV

Part 3 (B)

Module 31

K-Means Clustering

  • Understanding the working of K-Means Clustering 
  • Understanding of Elbow method to find optimal number of clusters
  • Implementation of K-Means Clustering 
  • Project - Shopping dataset Clustering

Module 32

Hierarchical & DBSCAN Clustering

  • Understanding and Implementation the working Hierarchical Clustering 
  • Understanding of Agglomerative Hierarchical (Single Linkage, Complete Linkage) 
  • Understanding and Implementation the working DBSCAN Clustering 
  • Project - Shopping dataset Clustering

Module 33

Association Rule Learning

  • Understanding the working of Association Rule Learning 
  • Understanding and Implementation of Apriori , Frequent Pattern Growth Algorithm 
  • Project - Market Basket Analysis

Module 34

PCA (Principal Component Analysis)

  • Understanding the working of PCA 
  • Understanding Eigen values and Eigen vectors 
  • Implementation of PCA

Part 4

Module 35

Neural Network & TensorFlow

  • Introduction to Neural Network 
  • What is a Neuron? 
  • Working of a Neuron 
  • Perceptron Model 
  • Concept of Hidden layers and Weights
  • Concept of Activation Functions, Optimizers and Loss Functions 
  • Equation of a General Neural Network 
  • Understanding Backpropagation 
  • Introduction to TensorFlow


Module 36

ANN (Artificial Neural Network)

  • Implementation of a Neural Network 
  • Implementation of ANN for Regression
  • Implementation of ANN for Classification 
  • Project - Customer Churn Modelling

Module 37

CNN (Convolutional Neural Network)

  • Understanding CNN (Convolutional Neural Network) 
  • Understanding the Convolution process 
  • Concept of Filter, strides 
  • Pooling Layer 
  • Fully Connected Layer 
  • Project - MNIST Image Classification

Part 5

Module 38

Natural Language Processing

  • Introduction to NLP, NLP Pipeline 
  • Removing Stop Words, Stemming, Lemmatization 
  • Count Vectorizer and Tf-Idf 
  • Word Sense Disambiguation, Tokenization, n - gram

Module 39

Image Processing using OpenCV

  • Reading and displaying an image using OpenCV 
  • Image Transformation operations 
  • Arithmetic Operations on Images 
  • Draw Line , Rectangle, Circle, Ellipse and Polygons on Image 
  • Object Detection using Haarcascade Files - Face and car Detection

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