Glory Technologies

Contact Number: +91-8008627755

Subject: Data Analytics

Duration: 60 Days

Timings: 8am-9am

1.Python

  1. Introduction and Basics of Python
  2. Operators
  3. Conditional Statements
  4. While Loops
  5. Lists
  6. Strings
  7. For Loop
  8. Functions
  9. Dictionary
  10. Tuples
  11. Set
  12. Object-Oriented Programming
  13. File Handling
  14. Exception Handling
  15. Regular Expression
  16. Modules and Packages
  17. Statistics with NumPy
  18. Data Analysis with Pandas Data Visualization with Matplotlib
  19. What to do Now?
  • Libraries for Data Science: Pandas, Numpy, SciPy

 2. Mathematics and Statistics  

  • Probability
    1. Definitions and Basic Concepts
    2. Probability Rules
    3. Conditional Probability
    4. Bayes’ Theorem
    5. Probability Distributions (Discrete and Continuous)
    6. Random Variables and Expected Value
  • Descriptive Statistics
  • 7. Measures of Central Tendency (Mean, Median, Mode)
    8. Measures of Dispersion (Range, Variance, Standard Deviation)
    9. Measures of Shape (Skewness, Kurtosis)
    10. Data Visualization (Histograms, Box Plots, Bar Charts)
    11. Data Summarization Techniques (Tabulation, Frequency Distribution)
  1. Data Visualization (Histograms, Box Plots, Bar Charts)
  2. Data Summarization Techniques (Tabulation, Frequency Distribution)
  • Inferential Statistics
    1. Sampling and Sampling Distributions
    2. Hypothesis Testing
    3. Confidence Intervals
    4. Regression Analysis
    5. Analysis of Variance (ANOVA)
    6. Chi-Square Test
    7. t-Tests (One-sample, Two-sample, Paired-sample)

3. Data Visualization

  • Power Bi
    1. Introduction to Power BI
    2. Power BI Components
    3. Power BI Desktop
    4. Data sources in Power BI
    5. Data Transformations in Power Query
  1. Visualization Techniques
  2. Creating Reports
  3. Dashboards in Power BI
  4. Power BI Service
  5. Sharing and Collaboration
  6. Power BI Integration with Other Tools
  • Matplotlib
  • Seaborn

4. Databases and SQL

  • Introduction to Databases
    1. What is a Database?
    2. Types of Databases (Relational vs. Non-Relational)
    3. Database Management Systems (DBMS)
  • Relational Databases
    1. Introduction to Relational Databases
    2. Tables, Rows, and Columns
    3. Primary Keys and Foreign Keys
    4. Relationships (One-to-One, One-to-Many, Many-to-Many)
    5. Normalization (1NF, 2NF, 3NF)
    6. Indexes and Constraints
    7. ACID Properties (Atomicity, Consistency, Isolation, Durability)
  • SQL Querying
    1. Introduction to SQL
    2. Basic SQL Commands (SELECT, INSERT, UPDATE, DELETE)
    3. SQL Clauses (WHERE, ORDER BY, GROUP BY, HAVING)
    4. SQL Functions (Aggregate Functions, String Functions, Date Functions)
    5. Joins in SQL (INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN)
    6. Subqueries and Nested Queries
    7. Triggers in SQL
    8. Transactions and Rollbacks
    9. Indexing and Performance Tuning
    10. SQL Security (Permissions, Roles, Encryption

5. Exploratory Data Analysis (EDA)

  • Data Cleaning
    1. Identifying and Handling Missing Data
    2. Dealing with Outliers
    3. Removing Duplicates
    4. Data Type Conversion
    5. Handling Inconsistent Data
    6. Standardizing Data
    7. Data Normalization and Scaling
    8. Addressing Data Entry Errors
  • Data Profiling
    1. Summary Statistics
    2. Data Distribution Analysis
    3. Correlation Analysis
    4. Data Quality Assessment
  1. Frequency Distribution for Categorical Data
  2. Data Completeness Check
  3. Identifying Relationships in Data

    6.Feature Engineering

    1. Creating New Features
    2. Feature Transformation
    3. Encoding Categorical Variables
    4. Binning and Discretization
    5. Handling Date and Time Features
    6. Feature Scaling and Normalization
    7. Feature Selection and Dimensionality Reduction

7. Data Analyst Projects

  • Capstone Projects
  • End-to-End Data Analyst Workflows

8. Soft Skills and Career Preparation

  • Communication Skills
  • Collaboration Tool: Git
  • Resume Writing and Interview Preparation
  • Weekly tests to monitor progress.
  • Daily notes and PDFs for thorough revision.
  • End-to-end data science projects for practical experience.
  • Resume preparation workshops.
  • Trainer available for doubts on WhatsApp group.
  • Interview questions and answers for job readines

***************📊 What is Data Analytics?********************************************

Data Analytics is the process of examining large datasets to uncover patterns, correlations, trends, and useful insights. It involves using statistical, computational, and machine learning techniques to analyze data and transform it into actionable insights for business decision-making, problem-solving, or predictions. Data analytics plays a vital role in a wide variety of industries, including finance, healthcare, marketing, and technology.

🛠️ Types of Data Analytics:

  1. Descriptive Analytics:

    • Purpose: To describe and summarize the characteristics of a dataset.
    • Example: Reporting on sales performance over the last quarter.
    • Techniques Used: Data aggregation, summarization, and basic statistics (mean, median, mode, standard deviation).

  2. Diagnostic Analytics:

    • Purpose: To understand the causes behind past trends or behaviors.
    • Example: Investigating why sales dropped last month.
    • Techniques Used: Root cause analysis, correlation analysis, and data exploration.

  3. Predictive Analytics:

    • Purpose: To predict future trends and outcomes based on historical data.
    • Example: Forecasting next month’s sales based on past data.
    • Techniques Used: Machine learning models (e.g., regression, time-series forecasting), statistical modeling, and decision trees.

  4. Prescriptive Analytics:

    • Purpose: To recommend actions that can optimize outcomes or solve problems.
    • Example: Recommending marketing strategies to boost customer engagement.
    • Techniques Used: Optimization algorithms, simulations, decision models.

  5. Cognitive Analytics:

    • Purpose: To use AI-driven insights that mimic human decision-making.
    • Example: Using AI to recognize customer sentiment from text data (e.g., social media).
    • Techniques Used: Natural Language Processing (NLP), deep learning, and AI algorithms.

💡 Key Steps in Data Analytics Process:

  1. Data Collection:
    Gather data from various sources like databases, APIs, spreadsheets, or sensors.

  2. Data Cleaning:
    Handle missing data, remove duplicates, and correct errors to ensure the data is accurate and usable.

  3. Data Exploration:
    Analyze and visualize the data to understand its structure, relationships, and distributions.

  4. Data Analysis:
    Apply statistical methods, machine learning, or business intelligence tools to extract meaningful patterns.

  5. Data Interpretation:
    Interpret the results in the context of the problem or business question being addressed.

  6. Reporting & Visualization:
    Create reports and visualizations (charts, dashboards) to communicate insights to stakeholders.