We are the Best Data Analytics and Data Science Courses/Training Providers across India

  • LOWEST FEES IN INDIA WITH INSTALLMENTS
  • We Teach in your Language (English/Hindi/Marathi)
  • One-to-one Teaching
  • Learn from Anywhere as ABC has Multiple Branches across India- Pune, Mumbai, Kolkata, Lucknow, Sangli, and Others
  • We schedule our Class as per your Time Convenience
  • We provide 24×7 Guidance and Support
  • If you are not willing to learn the Whole Course, you can Customize your Syllabus and Topics
  • Everyday Interview Trainings
  • Everyday Communication Training
  • CV and Portfolio Building
  • Innumerable Assignments
  • Daily Company Projects
  • Internships
  • We are #1 Awarded Company, Learn from us and Work with us
  • FREE Market Research Course, FREE Research Analyst Course, and FREE Business Development Course
  • Pay After Placement
  • 100% Placement Guarantee
  • FREE Demo Lectures
  • Our Teaching Faculty includes IITians
  • Freelancing and Start-up Guidance & Support
  • Support for Life
  • Major Certificates
  • 99% Practical, 1% Theory
  • Completion of Syllabus on Time
  • One Batch consists Limited Number of Students
  • Lecture Recordings are provided

DATA ANALYTICS AND DATA SCIENCE COURSE WITH 100% PLACEMENT GUARANTEE

ABC offers the Best Data Analytics Course with Placement Guarantee and the Best Data Science Course with Placement Guarantee anywhere across India. We offers Best Training at the LOWEST FEES IN INDIA WITH INSTALLMENTS. Not only this, but there are unbelievable benefits of chossing us: 1) We Teach in your Language (English/Hindi/Marathi) 2) One-to-one Teaching 3) Learn from Anywhere as ABC has Multiple Branches across India 4) We schedule our Class as per your Time Convenience 5) We provide 24×7 Guidance and Support 6) If you are not willing to learn the Whole Course, you can Customize your Syllabus and Topics 7) Everyday Interview Trainings 8) Everyday Communication Training 9) CV and Portfolio Building 10) Innumerable Assignments 11) Daily Company Projects 12) Internships 13) We are #1 Awarded Company, Learn from us and Work with us 14) FREE Market Research Course, FREE Research Analyst Course, and FREE Business Development Course 15) Pay After Placement 16) 100% Placement Guarantee 17) FREE Demo Lectures 18) Our Teaching Faculty includes IITians 19) Freelancing and Start-up Guidance & Support 20) Support for Life 21) 99% Practical, 1% Theory 22) Major Certificates

Data Analytics and Data Science Courses/Training Major Topics

We cover all the below pointers in our Data Analytics and Data Science Courses:

  1. Python
  2. R
  3. SQL
  4. Power BI
  5. Excel
  6. Google Analytics
  7. Artificial Intelligence
  8. Machine Learning
  9. FREE Market Research Course
  10. FREE Research Analyst Course
  11. FREE Business Development Course
  12. Customized Syllabus
  13. Freelancing & Start-up Chapters
More information on Data Analytics and Data Science Courses

Data Analytics Training Top Modules

  • Introduction to Data Analytics

    • Description: This module introduces the field of data analytics, including its importance and the different areas it covers.
    • Topics Covered:
      • Types of Data (Structured, Unstructured, Semi-structured)
      • The Analytics Process (Data Collection, Preparation, Analysis, Visualization, Reporting)
      • Overview of Data Analytics Roles (Data Analyst, Data Scientist, Business Intelligence Analyst)
      • The Impact of Data Analytics in Various Industries

  • Data Collection and Data Sources

    • Description: This module teaches how data is collected from various sources, cleaned, and organized for further analysis.
    • Topics Covered:
      • Types of Data Sources (Internal vs. External)
      • Data Collection Methods (Surveys, Web Scraping, APIs, Databases)
      • Data Privacy and Ethics in Data Collection
      • Data Formats (CSV, Excel, JSON, XML)

  • Data Cleaning and Preprocessing

    • Description: This is one of the most critical modules in data analytics, focusing on cleaning and preprocessing data to make it usable for analysis.
    • Topics Covered:
      • Handling Missing Data (Imputation, Deletion, Replacement)
      • Removing Outliers and Duplicates
      • Data Transformation (Normalization, Standardization)
      • Feature Engineering (Creating New Features from Existing Data)
      • Data Type Conversion (Categorical to Numerical, Date Parsing)

  • Exploratory Data Analysis (EDA)

    • Description: This module introduces methods for exploring and visualizing data, uncovering patterns, trends, and relationships within the dataset.
    • Topics Covered:
      • Summary Statistics (Mean, Median, Mode, Variance, Standard Deviation)
      • Data Visualization Techniques (Boxplots, Histograms, Scatter Plots)
      • Correlation Analysis (Heatmaps, Correlation Matrices)
      • Detecting Data Distribution and Trends
      • Identifying Patterns and Anomalies

  • Statistical Analysis and Hypothesis Testing

    • Description: In this module, learners are introduced to statistical methods that help in making data-driven decisions and validating hypotheses.
    • Topics Covered:
      • Descriptive vs. Inferential Statistics
      • Probability Distributions (Normal, Binomial, Poisson)
      • Hypothesis Testing (T-tests, Chi-square tests)
      • Confidence Intervals and P-values
      • ANOVA (Analysis of Variance)

  • Machine Learning for Data Analytics

    • Description: This module focuses on the application of machine learning algorithms to predict trends and make data-driven decisions.
    • Topics Covered:
      • Supervised Learning Algorithms (Linear Regression, Logistic Regression, Decision Trees)
      • Unsupervised Learning Algorithms (K-means Clustering, PCA)
      • Model Training and Testing
      • Model Evaluation Metrics (Accuracy, Precision, Recall, F1 Score)
      • Overfitting and Underfitting

  • Time Series Analysis

    • Description: Time series analysis involves working with data that is indexed by time, and it is crucial for forecasting and trend analysis.
    • Topics Covered:
      • Time Series Decomposition (Trend, Seasonal, Residual)
      • Autoregressive Models (ARIMA, Exponential Smoothing)
      • Forecasting and Prediction Techniques
      • Dealing with Seasonality and Trends
      • Evaluating Forecasting Accuracy (RMSE, MAE)

  • Data Visualization and Reporting

    • Description: This module teaches how to present data insights in an easy-to-understand format using visualization tools.
    • Topics Covered:
      • Types of Charts and Visualizations (Bar charts, Line graphs, Heatmaps, Pie charts)
      • Designing Interactive Dashboards
      • Tools for Visualization (Tableau, Power BI, Matplotlib, Seaborn)
      • Best Practices for Data Storytelling and Report Writing
      • Communicating Insights to Stakeholders

  • Business Intelligence (BI) and Data Analytics

    • Description: This module focuses on how analytics tools help in business decision-making by providing valuable insights.
    • Topics Covered:
      • Introduction to Business Intelligence (BI)
      • BI Tools and Dashboards (Power BI, Tableau)
      • Key Performance Indicators (KPIs) and Metrics
      • Data Warehousing and ETL Processes
      • BI Reporting and Interactive Dashboards

  • Big Data and Advanced Analytics

    • Description: This module covers the techniques for working with large-scale data, including the use of big data platforms and advanced analytical techniques.
    • Topics Covered:
      • Introduction to Big Data (Volume, Velocity, Variety)
      • Tools and Technologies for Big Data (Hadoop, Spark)
      • Data Storage Solutions (HDFS, NoSQL databases)
      • Distributed Computing and Parallel Processing
      • Machine Learning on Big Data.

        Some of the Live Data Analytics Projects by Our Students

        1. Sales Performance Analysis
          Analyze sales data to track performance trends, identify growth opportunities, and predict future sales.

        2. Customer Lifetime Value Prediction
          Predict the total value a customer will bring over their entire relationship with a business to optimize marketing spend.

        3. Customer Segmentation for Targeted Marketing
          Use clustering techniques to segment customers based on purchasing behavior for more targeted marketing campaigns.

        4. Sentiment Analysis of Product Reviews
          Analyze customer reviews from e-commerce platforms to determine the sentiment (positive, negative, neutral) of products.

        5. Churn Prediction for Telecom Industry
          Predict which telecom customers are likely to leave the service to enable proactive retention strategies.

        6. Retail Inventory Optimization
          Use data to forecast inventory needs, minimizing overstock or stockouts, and improving supply chain management.

        7. Fraud Detection in Banking Transactions
          Identify unusual patterns in transaction data to detect and prevent fraudulent activities in real-time.

        8. Predictive Maintenance for Manufacturing Equipment
          Predict machine failures before they occur, reducing downtime and maintenance costs in manufacturing plants.

        9. Energy Consumption Forecasting
          Use historical data to forecast energy demand, helping utility companies optimize energy distribution.

        10. Real-Time Traffic Prediction for Smart Cities
          Analyze traffic data in real-time to predict and manage traffic flow, helping reduce congestion and improve road safety.

        11. Social Media Analytics for Brand Monitoring
          Track brand mentions across social media platforms to assess public sentiment and improve brand image.

        12. Healthcare Diagnosis Prediction
          Analyze patient data to predict the likelihood of diseases or conditions, improving early diagnosis and treatment.

        13. Stock Market Trend Analysis
          Use historical stock price data to predict future stock movements, helping investors make informed decisions.

        14. Employee Performance Analytics
          Analyze employee data to track performance trends and identify factors affecting productivity.

        15. Sports Analytics for Player Performance
          Use player statistics and game data to evaluate performance, injury risks, and optimal strategies for sports teams.

Data Analytics Tools and Chapters

  1. Microsoft Excel for Data Analysis

    • Description: Microsoft Excel is widely used for data analysis due to its user-friendly interface and powerful features like formulas, pivot tables, and charts.
    • Topics Covered:
      • Data cleaning, sorting, and filtering
      • Creating PivotTables and PivotCharts for data summarization and visualization
      • Using advanced functions like VLOOKUP, INDEX-MATCH
      • Statistical analysis tools (Descriptive statistics, Regression)
      • Conditional formatting and data visualization techniques

  2. SQL for Data Analytics

    • Description: SQL is the go-to language for querying and managing structured data stored in relational databases. It’s essential for extracting insights from large datasets.
    • Topics Covered:
      • Writing SQL queries (SELECT, WHERE, JOIN, GROUP BY)
      • Aggregate functions (COUNT, SUM, AVG)
      • Subqueries and nested queries
      • Filtering, sorting, and grouping data
      • Optimizing database queries for better performance

  3. Tableau for Data Visualization

    • Description: Tableau is a powerful tool for creating interactive data visualizations and dashboards that provide actionable insights from complex datasets.
    • Topics Covered:
      • Connecting to different data sources (Excel, SQL, Cloud Services)
      • Creating interactive and dynamic dashboards
      • Customizing visualizations (Bar charts, line graphs, maps)
      • Using calculated fields for advanced data analysis
      • Publishing and sharing visualizations with Tableau Server/Online

  4. Power BI for Business Intelligence

    • Description: Power BI is a Microsoft tool that helps users analyze data and share insights through interactive visualizations and dashboards.
    • Topics Covered:
      • Power BI Desktop and Power BI Service
      • Importing data and transforming it with Power Query Editor
      • Creating interactive reports and visualizations
      • Using DAX for custom calculations and advanced analytics
      • Sharing and collaborating on reports in Power BI Service

  5. Python for Data Analytics

    • Description: Python is one of the most popular programming languages for data analysis, with powerful libraries for data manipulation, visualization, and machine learning.
    • Topics Covered:
      • Data manipulation with Pandas (DataFrames, merging, filtering)
      • Data visualization using Matplotlib and Seaborn
      • Numerical analysis with NumPy
      • Statistical analysis with SciPy and statsmodels
      • Basic machine learning with scikit-learn (classification, regression)

  6. R for Data Analysis

    • Description: R is a programming language and software environment for statistical computing and graphics, widely used by data scientists for data analysis.
    • Topics Covered:
      • Data manipulation with dplyr and tidyr
      • Data visualization with ggplot2
      • Statistical modeling and hypothesis testing
      • Time series analysis and forecasting
      • Machine learning techniques using caret and randomForest

  7. Google Analytics for Web Analytics

    • Description: Google Analytics helps track and analyze website traffic, user behavior, and conversions to make informed business decisions.
    • Topics Covered:
      • Setting up Google Analytics and configuring tracking codes
      • Understanding and interpreting traffic metrics (sessions, bounce rate, etc.)
      • Creating custom reports and dashboards
      • Goal tracking and event tracking for user interaction analysis
      • Integration with other tools like Google Tag Manager and Google Data Studio.

AI-Related Tools in Data Analytics

  1. Excel with Data Analysis ToolPak (AI features)

    • Description: Excel offers built-in tools like the Data Analysis ToolPak for statistical analysis, along with basic machine learning capabilities like trendlines, regression, and forecasting.
    • Topics Covered:
      • Linear regression analysis and trend forecasting: Using Excel to perform regression analysis for predictive modeling.
      • Moving averages and exponential smoothing for time series forecasting: Applying smoothing techniques to forecast future data trends.
      • Data mining tools (cluster analysis, correlation): Discovering patterns in large datasets using built-in Excel functions.
      • Predictive modeling using Excel add-ins (e.g., Solver and Power Query): Using add-ins to build optimization models and solve data problems.
      • Basic AI-driven Excel features like automatic data categorization: Leveraging Excel’s AI features for automatic data categorization and sorting.

  2. Google Sheets with Google AI Add-ons

    • Description: Google Sheets can integrate with Google’s AI and machine learning add-ons to perform basic AI-driven analytics such as data predictions, sentiment analysis, and data clustering.
    • Topics Covered:
      • Using Google Sheets add-ons for sentiment analysis (e.g., Natural Language API): Integrating Google’s NLP tools to analyze text data for sentiment.
      • Using Google Cloud AutoML for simple predictive modeling: Utilizing AutoML tools within Google Sheets to automate predictive modeling tasks.
      • Integrating machine learning models with Google Sheets for automated insights: Connecting Google Sheets with machine learning models for real-time data-driven insights.
      • Data visualization with AI-powered charts: Creating dynamic visualizations powered by AI to represent data in Google Sheets.
      • Setting up custom formulas for basic predictions (e.g., stock forecasting): Using formulas to predict trends based on historical data.

  3. Python’s scikit-learn for Basic AI Models

    • Description: scikit-learn is a Python library that provides simple, easy-to-use tools for implementing basic machine learning algorithms like classification, regression, and clustering.
    • Topics Covered:
      • Implementing basic machine learning models (Logistic Regression, k-NN, Decision Trees): Building fundamental machine learning models for classification and regression tasks.
      • Data preprocessing and feature scaling with scikit-learn: Transforming raw data into a suitable format for machine learning.
      • Splitting data into training and test sets for model evaluation: Learning how to split datasets to evaluate model performance effectively.
      • Model performance metrics (accuracy, precision, recall): Evaluating the performance of models using standard metrics.
      • Simple data classification and clustering tasks: Applying clustering and classification techniques to real-world data problems.

  4. RapidMiner for AI-Powered Analytics

    • Description: RapidMiner is a no-code AI and machine learning platform that enables users to build predictive models without requiring advanced programming skills.
    • Topics Covered:
      • Building machine learning models using a drag-and-drop interface: Using RapidMiner’s visual interface to design machine learning workflows.
      • Performing data preprocessing and feature engineering: Cleaning and preparing data for machine learning tasks.
      • Deploying machine learning models for predictive analytics: Deploying models created in RapidMiner to make predictions.
      • Analyzing model performance with built-in metrics: Using RapidMiner’s built-in tools to evaluate the accuracy and performance of models.
      • Using RapidMiner for AI-driven clustering, regression, and classification: Applying AI models for a wide range of analytics tasks like clustering, regression, and classification.