Data Science Full Course
Data science is an interdisciplinary field that involves the use of scientific methods, algorithms, processes, and systems to extract valuable insights and knowledge from structured and unstructured data. It combines elements from statistics, computer science, domain expertise, and data visualization to analyze data, uncover patterns, make predictions, and inform decision-making in various industries and domains. Data scientists utilize a range of tools and techniques to collect, clean, explore, and model data, ultimately helping organizations make data-driven decisions and solve complex problems.
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Course Overview
Data science is the study of data to extract meaningful insights for business. It is a multidisciplinary approach that combines principles and practices from the fields of mathematics, statistics, artificial intelligence, and computer engineering to analyze large amounts of data.
Schedule of Classes
Start Date & End Date
Sep 12 2023 - Dec 31 2023
Total Classes
95 Classes
Course Curriculum
1 Subject
Data Science
30 Exercises • 93 Learning Materials
Python
Python introduction, installation
Variables, Data Types (Numbers, strings and Boolean)
Built in Functions, Operators (Various operators and examples)
Type conversions, user input and output with formatting
Control flow (conditional statements, nested conditions and loops and nested loops)
Data Structures
Functions (user defined functions, arguments, variable length args and kwargs)
List Comprehensions, Filter Map and Reduce
Python
Python Assignments-1
Python Assignment-2
NumPy
Introduction to NumPy (array processing using NumPy)
Creating, Accessing and Modifying NumPy Arrays
Saving and Loading NumPy arrays
Indexing and Slicing operations on arrays
Operations on 1D,2D arrays, Arithmetic Functions
Concatenation, stacking, splitting and iterating over arrays
Numpy
Numpy Assignment
Numpy Assignment 2
Pandas
Introduction to Pandas (Series and Data Frames)
Creating, Accessing and Modifying Pandas Data Frames
Saving and Loading Pandas Data Frames
Levels of access, filtering and manipulating Data Frames
Indexing, sorting, ranking, joins, groups, Merging and Joining, Reshaping, Pivot Table
Pandas
Pandas Assignment-1
Pandas Assignment-2
Visual Data Analysis
Introduction to Plotting Libraries
Plots using data frame, seaborn, matplotlib and plotly
The plotting architecture
Summary statistics and correlations
Understand, comparing and observations using plotting
Plots with respect to various types of data
Various plots and its usage
Interactive plots, annotation heading, labelling and markings
Visual Data Analysis
Visual Data Analysis Assignment-1
Visual Data Analysis Assignment-2
Exploratory Data Analysis
Understand the importance of EDA
Classification of Data, Understanding the variables
Descriptive Statistics
Measure of Central Tendency
Distribution of the data
Measure of Dispersion
Univariate Analysis, Multivariate Analysis
Handling Non-Numeric Data
Feature Scaling and Data Transformation
Data Type Conversion
Types of Bivariate Analysis
Quantitative - Quantitative
Quantitative - Categorical
Categorical – Categorical
Missing Value Treatment
Outlier Treatment - (Z-score, Interquartile Range)
Feature Engineering, Train-Test Split
Exploratory Data Analysis Assignment-1
Exploratory Data Analysis
Exploratory Data Analysis Assignment-2
Statistical Data Analysis
Random Variable
Probability Distribution
Discrete Probability Distribution (Binomial Distribution)
Continuous Probability Distribution (Normal Distribution)
Population and Sample, Sampling Techniques, Sampling Distribution
Theory of Estimation (Point estimation, Sampling error, Interval estimation)
Central Limit Theorem and its importance in role in Hypothesis Testing
Hypothesis Testing
Large Sample Test (Two Sample Z test)
Small Sample Test (One Sample, Two Sample)
Test for Population Proportion (One Sample, Two Sample)
Chi-square test (Goodness of Fit, Independence of Attributes)
Analysis of variance (One way ANOVA)
Statistical Data Analysis
Statistical Data Analysis Assignment-1
Statistical Data Analysis Assignment-2
Machine Learning
Introduction to Machine Learning
Types of Machine Learning (supervised, unsupervised)
Importance of machine learning and its usage in various industries
Machine Learning Skill Test
Machine Learning Assignment-1
Machine Learning Assignment-2
Regression
Regression Analysis, Covariance and Correlation
Ordinary Least Squares Method
Measures of Variation, Inferences about slope
Assumptions of Linear Regression
Model Evaluation Metrics, Optimization Algorithm
Machine Learning Pipeline
Feature Extraction, Feature Transformation, Feature Engineering, Feature Selection
Optimization, Prediction Evaluation
Model Validation, Fine Tuning Models
Gradient Descent (Batch Gradient Descent, Stochastic Gradient Descent, Mini Batch Gradient Descent)
Regularization (Ridge Regression, Lasso Regression, Elastic-Net Regression
Grid Search, KFLod, Cross Validation
Regression
Regression Assignment-1
Regression Assignment-2
Classification
Supervised Learning, Classification
Standard Process for Data Science Project
Binomial Logistic Regression, Assumptions of Logistic Regression, Significance of Coefficients
Model Evaluation Metrics, Model Performance Measures
Imbalanced Data, handling balance, up sampling and down sampling
Decision Trees for Classification (The measure of Purity of Node, Construction of Decision Tree)
Decision Tree Algorithms, Overfitting in a Decision Tree
Ensemble Learning, Random Forest Classifier, Feature Importance
KNN Algorithm, proximity measures
Ensemble Techniques (Bagging & Boosting Algorithms)
Ada Boost, Gradient Boosting, AdaBoost Vs Gradient Boosting, XGBoost
Stack Generalization and voting classifier
Classification
Classification Assignment-1
Classification Assignment-2
Deep Learning
Introduction to Neural Networks, Perceptron learning
Introduction to Artificial Neural Networks, Delta Learning Rule, Error Back Propagation
Extending the idea of ANN for regression and classification
Working with Images,Videos,Text , Time Series Data, Image Basics, Handling and preprocessing
Introduction to 1-D,2-DConvolutional Neural Networks, Filters, Stride, Maxpooling and Flattening
Introduction to TensorFlow and Keras and Torch Frameworks for Deep Learning
Building and Training a Neural Networks from scratch
Understanding Pre-Trained Classification Networks and Transfer Learning
Training a model on Custom Data Set – Model Tuning, Performance Evaluation model save points
Deep learning
Deep Learning Assignment-1
Deep Learning Assignment-2
Data Science
Course Instructor
Panduranga Reddy
2 Courses • 15 Students
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