Data Analytics
A Data Analytics course equips students with skills to extract, clean, and interpret data for informed decision-making. It begins with data collection and preprocessing, teaching how to manage various data sources and formats. Students learn statistical techniques and tools like Excel, SQL, and Python or R for data manipulation and visualization. Key concepts include exploratory data analysis (EDA), hypothesis testing, and regression analysis. Advanced topics cover machine learning, data mining, and big data technologies. Practical exercises and projects, such as analyzing real-world datasets and creating dashboards, solidify knowledge. By course end, participants can conduct comprehensive data analyses, draw meaningful insights, and present findings effectively to support strategic decisions.
Duration - 6 Months
Definition and Importance
Data Analytics Lifecycle
Applications of Data Analytics in Various Industries
Installing Python
Introduction to Jupyter Notebooks
Installing Required Libraries (NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn)
Variables and Data Types
Basic Operators
Conditional Statements
Loops
Lists
Tuples
Dictionaries
Sets
Defining and Calling Functions
Importing Modules
Using Built-in Functions and Libraries
Data Collection
Importing Data from CSV, Excel, and SQL Databases
Web Scraping Basics with BeautifulSoup and Scrapy
Using APIs for Data Collection (requests)
Data Wrangling
Handling Missing Values
Data Cleaning Techniques
Data Transformation and Normalization
Combining and Merging Datasets
Introduction to EDA
Importance of EDA
Steps in EDA
Descriptive Statistics
Measures of Central Tendency (Mean, Median, Mode)
Measures of Dispersion (Standard Deviation, Variance, Range)
Skewness and Kurtosis
Data Visualization
Introduction to Matplotlib and Seaborn
Creating Basic Plots (Line, Bar, Histogram, Box Plot)
Customizing Plots (Titles, Labels, Legends)
Introduction to Pandas
Series and DataFrame Basics
Importing and Exporting Data
Data Manipulation
Filtering and Sorting Data
Grouping and Aggregation
Handling Time Series Data
Applying Functions to DataFrames
Introduction to NumPy
Arrays and Matrices
Basic Operations on Arrays
Advanced NumPy Techniques
Broadcasting
Vectorization
Linear Algebra Operations
Advanced Data Visualization
Heatmaps
Pair Plots
Violin Plots
Faceting with Seaborn
Interactive Visualizations
Using Plotly for Interactive Charts
Creating Dashboards with Plotly Dash
Introduction to Statistics
Probability Theory
Random Variables and Probability Distributions
Hypothesis Testing
Null and Alternative Hypotheses
Types of Tests (T-test, Chi-square test)
P-values and Significance Levels
Overview of Machine Learning
Supervised vs. Unsupervised Learning
Common Machine Learning Algorithms
Implementing Machine Learning Models
Data Preprocessing for Machine Learning
Training and Testing Models
Model Evaluation Metrics
Supervised Learning
Linear Regression
Logistic Regression
Decision Trees
Random Forests
Support Vector Machines (SVM)
Unsupervised Learning
K-Means Clustering
Hierarchical Clustering
Principal Component Analysis (PCA)
Introduction to Time Series
Components of Time Series Data
Moving Averages and Smoothing
Advanced Time Series Techniques
Autoregressive Integrated Moving Average (ARIMA)
Seasonal Decomposition
Forecasting
Function Syntax
Calling Functions
Function Arguments and Return Values
Default and Keyword Arguments
Lambda Functions
Scope and Lifetime of Variables
Introduction to Big Data
Definition and Characteristics of Big Data
Big Data Technologies
Working with Hadoop
Hadoop Ecosystem Overview
HDFS (Hadoop Distributed File System)
MapReduce Basics
Practice Exercises for Each Module
Real-world Problem Solving
Analyzing Real-world Datasets
Interpreting Results and Drawing Conclusions
Building Small Data Analysis Applications
Implementing Data Processing Pipelines
Comprehensive Project Covering Multiple Modules
Real-world Problem Solving and Implementation