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+91-834 161 1955 Mon-Fri 9am - 6pm
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Data Analytics Training Key Features

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Practical Data Analysis Labs

Get hands-on experience with diverse datasets, utilizing essential tools like Excel, SQL, Power BI, and Tableau in our dedicated lab environment.

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Flexible Online and In-Person Classes

Learn at your convenience through our classroom sessions at Ameerpet or Kukatpally, or join live interactive online classes from anywhere in the world.

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Dedicated Data Analytics Mentorship

Receive personalized assistance for all your data projects and complex analytical queries from our experienced instructors during and after your course.

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Robust Career & Placement Guidance

We help you prepare for data analyst interviews with mock sessions, resume optimization, and direct connections to job opportunities in leading companies.

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Real-World Data Projects

Gain invaluable experience by working on end-to-end data analysis projects, from data cleaning and transformation to insightful reporting and presentation.

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Engaging Learning Community

Collaborate with a supportive community of peers and instructors, fostering enhanced analytical skills, knowledge sharing, and valuable networking opportunities.

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Data Analytics Training Overview

Value Learning offers comprehensive Data Analytics training courses at both Ameerpet and Kukatpally (KPHB), Hyderabad. Our programs are meticulously designed to equip you with the practical skills and analytical capabilities needed to interpret data and drive informed decision-making in various industries.

Data Analytics is the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. This field is crucial for businesses looking to gain strategic insights and optimize operations. Our expert-led training covers descriptive, diagnostic, predictive, and prescriptive analytics, utilizing popular tools like Excel, SQL, Power BI, and Tableau to ensure you are proficient in the entire data lifecycle.

320

Successful Learners

68k

Training Hours Delivered

540

Enterprise Projects Covered

Data Analytics Training Objectives

The Data Analytics course at Value Learning, delivered at our Ameerpet and Kukatpally (KPHB) centers in Hyderabad, is designed to give learners a robust understanding of data analysis techniques and the tools essential for interpreting complex datasets.

Through this training, you will gain hands-on experience with data manipulation, statistical analysis, and creating compelling data visualizations using industry-standard software. You'll learn how to extract actionable insights from raw data and present them effectively for business decision-making.

The primary goal of the training is to empower learners to confidently interpret complex datasets and translate data into strategic business decisions, preparing them for highly sought-after roles as data analysts in various industries.

To equip learners with comprehensive, practical experience in the end-to-end data analysis lifecycle, from data acquisition and cleaning to reporting and presentation, ensuring they can drive real value from organizational data.

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Course Curriculum - Data Analytics

Overview:
  • What is Data Analytics? Definition and Importance in Business
  • Understanding the Data Analytics Life Cycle (Ask, Prepare, Process, Analyze, Share, Act)
  • Distinction: Data Analytics vs. Data Science vs. Business Intelligence
  • Types of Analytics: Descriptive, Diagnostic, Predictive, Prescriptive
  • Career Opportunities in Data Analytics and its Growing Demand

  • Advanced Excel Functions (VLOOKUP, HLOOKUP, INDEX, MATCH, SUMIFS, COUNTIFS)
  • Data Validation and Conditional Formatting
  • Sorting, Filtering, and Freezing Panes for Data Exploration
  • Using Pivot Tables and Pivot Charts for Summarization
  • What-If Analysis, Goal Seek, and Solver Tools

  • Introduction to Relational Databases and SQL Concepts
  • Retrieving Data with SELECT, FROM, WHERE Clauses
  • Filtering Data with Operators (AND, OR, NOT, IN, LIKE, BETWEEN)
  • Sorting and Limiting Results (ORDER BY, LIMIT/TOP)
  • Aggregating Data with GROUP BY, HAVING (COUNT, SUM, AVG, MIN, MAX)
  • Joining Multiple Tables (INNER, LEFT, RIGHT, FULL OUTER JOIN)

  • Setting up Python Environment (Anaconda, Jupyter Notebook)
  • Python Basics: Data Types, Variables, Operators, Control Flow
  • Functions, Modules, and Object-Oriented Programming (OOP) concepts
  • Working with Lists, Tuples, Dictionaries, and Sets
  • Reading and Writing Data to Files (CSV, TXT, JSON)

  • Introduction to Pandas Series and DataFrames
  • Data Loading: CSV, Excel, SQL Databases into DataFrames
  • Indexing, Slicing, and Subsetting DataFrames
  • Handling Missing Values (NaN) and Duplicate Records
  • Data Cleaning, Transformation, and Reshaping Techniques
  • Merging, Joining, and Concatenating DataFrames

  • Understanding Data Distributions and Summary Statistics
  • Identifying Outliers and Anomalies
  • Univariate, Bivariate, and Multivariate Analysis
  • Correlation Analysis between Variables
  • Practical Case Studies on EDA

  • Creating Basic Plots: Line Plots, Bar Charts, Histograms, Scatter Plots
  • Customizing Plot Aesthetics: Titles, Labels, Legends, Colors
  • Advanced Visualization with Seaborn: Heatmaps, Box Plots, Violin Plots
  • Plotting Distributions: Distplots, Pair Plots
  • Best Practices for Effective Data Storytelling through Visuals

  • Understanding BI Concepts: Data Warehousing, ETL Processes (overview)
  • Importance of Dashboards for Business Insights
  • Introduction to Popular BI Tools (Tableau/Power BI - conceptual)
  • Principles of Dashboard Design: Usability, Clarity, Interactivity
  • Creating Basic Interactive Dashboards (using either Tableau/Power BI depending on focus)

  • Basic Statistical Measures (Mean, Median, Mode, Standard Deviation)
  • Understanding Probability and Probability Distributions
  • Hypothesis Testing: T-tests, Chi-Squared Tests
  • A/B Testing Concepts and Interpretation
  • Introduction to Sampling and Inferential Statistics

  • Understanding Predictive Modeling Concepts
  • Simple Linear Regression for Trend Forecasting
  • Introduction to Time Series Analysis (basic concepts)
  • Decision Trees for Classification and Prediction (overview)
  • Understanding Model Accuracy and Overfitting (basic concepts)

  • Crafting a Compelling Narrative from Data Insights
  • Designing Effective Presentations for Diverse Audiences
  • Communicating Complex Analytical Findings Clearly
  • Highlighting Key Takeaways and Actionable Recommendations
  • Developing Strong Communication and Presentation Skills

  • Understanding Data Ethics: Bias, Fairness, Transparency
  • Introduction to Data Privacy Regulations (GDPR, CCPA - overview)
  • Securing Sensitive Data and Anonymization Techniques
  • Responsible Data Collection and Usage Practices
  • Building Trust and Maintaining Data Integrity

  • Introduction to Cloud Computing for Data Analytics
  • Overview of AWS, Azure, Google Cloud Data Services
  • Concepts of Cloud Data Warehousing (e.g., Snowflake, Redshift)
  • Using Cloud-based Notebook Environments
  • Scalability and Cost-effectiveness in Cloud Data Analytics

  • Introduction to Git and GitHub for Code Management
  • Branching, Merging, and Committing Changes
  • Collaborating on Data Projects with Version Control
  • Best Practices for Organizing Data Analysis Projects
  • Sharing Notebooks and Analytical Assets Effectively

  • End-to-End Data Analytics Project Implementation
  • Building a Professional Portfolio of Analytics Projects
  • Preparing for Data Analyst Interviews: Technical and Behavioral Questions
  • Job Roles and Opportunities in Data Analytics in Hyderabad
  • Continuing Education and Advanced Certifications
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