Data Analytics Course in Noida

Module 1: Introduction to Data Analytics [Duration: 3 hrs]

• Definition and Future Scope of Data Analytics
• Importance of Data in Decision-making processes
• Types of Data Analytics:
o Descriptive
o Diagnostic
o Predictive
o Prescriptive
• Data Analytics Process:
1) Defining the Problem
2) Data Collection
3) Data Cleaning and Preprocessing
4) Data Exploration and Analysis
5) Model Building
6) Implementation and Monitoring
• Common tools that are used in Data Analytics
o Excel
o R
o Python
o SQL
o Tableau
o Power BI
• Applications of Data Analytics across Various Industrial Sectors

Module 2: Statistics for Data Analytics [Duration: 6 hrs]

• Descriptive Statistics
o Measures of Central Tendency:
1) Mean
2) Median
3) Mode
o Measures of Dispersion:
1) Range
2) Variance
3) Standard Deviation
4) Interquartile Range(IQR)
Measure of Shape:
1) Skewness
2) Kurtosis
• Inferential Statistics
o Sampling Techniques –
1) Random Sampling
2) Sampling Bias
o Central Limit Theorem
o Confidence Intervals
o Hypothesis Testing:
1) H0
2) H1 or Hα
3) Test Static
4) P-value
5) Significance Level(α)
o T-tests
o Chi-square tests
o ANOVA
• Correlation vs Causation
• Statistical Significance and A/B Testing
• Application of Statistics in Real-world Problems

Module 3: Programming for Data Analytics [Duration: 6 hrs]

• Introduction to R and Python for Analytics
• Basics of Programming :
o Variables
o Data Types
o Operators
o Control Flow Structure –
1) Conditional Statements (if/elif/else)
2) Loops (for/while)
o Data Structures – (Lists, Tuples, Dictionaries, Sets)
o Functions
o Object Oriented Programming –
1) Classes and Objects
2) Methods
3) Inheritance
4) Polymorphism
5) Encapsulation
6) Error Handling
• Exploratory Data Analysis (EDA) with R and Python
• Working with Data using Libraries like below:
o Pandas
o Numpy
o Dplyr
o Matplotlib
o Seaborn
o Scikit-Learn
o Scipy
o SQLAlchemy
• Handling Data Input/Output
1) CSV
2) Excel
3) JSON
• Using RStudio and Jupyter Notebooks for Data Analysis

Module 4: R Packages [Duration: 2 hrs]

• Utilizing dplyr for Data Manipulation Tasks
o Data Cleaning
o Adding/Modifying Variables
o Joining Datasets
o Chaining Operations
• Tidying Data with tidyr: Reshaping and Transforming
o Wide-to-Long Reshaping
o Long-to-Wide Transformation
o Separating/Combining Columns
o Handling Missing Data
• Filtering, Selecting, Arranging, and Summarizing Data Efficiently
o Filtering Rows
o Selecting Columns
o Arranging Data
o Summarizing Data

Module 5: Data Manipulation and Transformation with R [Duration: 10 hrs]

• Understanding R syntax and IDE (RStudio) for data manipulation
• Data Structures in R :
o Vectors
o Lists
o Matrices
o Data Frames
• Data Wrangling using Tidyverse Packages (like dplyr, tidyr)
• Advanced Data Transformation:
o Merging
o Joining
o Reshaping
• Time-Series Data Manipulation
o Formatting Date-Time Objects
o Time-Based Filtering
o Aggregating Time-Series Data
o Handling Missing Time Points
o Time-Series Analysis
Handling Categorical and Text Data
o Categorical Data Transformation
o One-Hot Encoding
o Text Cleaning
o Pattern Matching
o Text Analysis
• Data Integration with Databases and External Files
o Connecting to Databases
o Reading External Files
o Writing Data
o Merging External Datasets

Module 6: Data Visualization for Analytics [Duration: 5 hrs]

• Importance of Data Visualization
• Visualization Tools and Libraries:
o Tableau
o Power BI
o ggplot2 (R)
o matplotlib/seaborn (Python)
• Creating Different Chart Types:
o Bar Graph
o Pie Chart
o Scatter Plot
o Histograms
o Box plots
• Dashboards and Interactive Visualizations:
o Tools for Building Dashboards
1) Shiny
2) Flexdashboard
3) Plotly and ggplot2 Integration
4) Dash (Python)
o Interactive Visualization Techniques
o Data Presentation
o Export and Sharing Options
• Data Storytelling:
o Interpreting Data Insights
o Presenting Visual Data Insights

Module 7: Data Acquisition and Cleaning [Duration: 6 hrs]

• Data Acquisition Methods:
o APIs
o Web Scraping
o SQL Databases
• Structured vs Unstructured Data
• Cleaning Data:
o Dealing with Missing Values
o Removing Duplicates
o Eradicating Inconsistencies
• Data type Conversions and Normalization
• Feature Creation and Transformation

Module 8: SQL for Data Analytics [Duration: 6 hrs]

• Basics of SQL:
o Relational Databases
o DDL Commands:
1) Create
2) Alter
3) Truncate
4) Drop
o DML Commands:
1) Insert
2) Update
3) Delete
o DQL Commands: Select
o SQL Clauses:
1) Where
2) Group By
3) Order By
SQL JOINS:
1) Inner Join/Join
2) Left join/Left Outer Join
3) Right Join/Right Outer Join
4) Full Join/Full Outer Join
5) Self Join
6) Cross Join
• Advanced SQL Queries:
o Subqueries
o Window Functions
o CTEs
• Data Aggregation and Filtering
• Working with Relational Databases and Connecting with R/Python

Module 9: Predictive Analytics [Duration: 5 hrs]

• Introduction to Predictive Modeling
• Linear and Logistic Regression
• Model Evaluation:
o RMSE
o MAE
o ROC-AUC
• Overfitting,
• Underfitting
• Model tuning
• Using Analytics Tools for Predictive Models:
o caret in R
o scikit-learn in Python

Module 10: Supervised Learning [Duration: 6 hrs]

• Understanding Supervised Learning Concepts and Objectives
o Learning Patterns
o Prediction
o Error Minimization
o Applications
• Implementing Regression Algorithms for Predicting Continuous Outcomes
o Linear Regression
o Polynomial Regression
o Ridge and Lasso Regression
o Evaluation Metrics
• Exploring Classification Algorithms for Predicting Categorical Outcomes
o Logistic Regression
o Decision Trees
o Random Forest
o Support Vector Machines (SVM)
o K-Nearest Neighbors (KNN)
o Evaluation Metrics

Module11: Clustering and Segmentation [Duration: 5 hrs]

• Introduction to Unsupervised Learning
• K-Means Clustering
• Hierarchical Clustering
• Cluster Validation Techniques :
o Elbow Method
o Silhouette Score
• Customer Segmentation
• Market Basket Analysis
• Usecases of Clustering in Business

Module 12: Natural Language Processing in Analytics [Duration: 6 hrs]

• Text Analytics Fundamentals
• Preprocessing Text:
o Tokenization
o Stopwords
o Stemming
o Lemmatization
• Sentiment Analysis using R/Python
• Text Classification
• Topic Modeling
• Visualizing Textual Data
o Word Clouds
o Frequency Plots
• Real-life Applications of NLP in Analytics

Module 13: Deep Learning for Analytics [Duration: 6 hrs]

• Basic Understanding of Deep Learning and Neural Networks
• Evolution of Deep Learning
• Exploring the Diverse Applications and Significance of Deep Learning across Various Domains
• Introduction to Artificial Neural Networks (ANNs) and their Architecture
• Understanding:
o Neurons
o Layers
o Weights
o Activation Functions
• Mechanisms of Feedforward algorithms
• Mechanisms of Backpropagation algorithms
Overview of Deep Learning Frameworks:
o TensorFlow
o PyTorch
• Constructing and Training Simple Neural Networks for Analytical Tasks
• Merits and Limitations of Deep Learning in Analytics