✅Best DATA Analyst Training Institute [Top-Rated & Certified]

COURSE OVERVIEW

A Data Analytics Training Institute offers specialized courses that teach data analysis, statistics, visualization, and database skills, enabling students to analyze data and support business decision-making using industry tools like Python, SQL, and Power BI.

Understand Data Analytics Fundamentals: Learn core analytics concepts, workflows, and real-world applications across various industries.
Develop Proficiency in Python for Data Analysis: Use Python, Pandas, and NumPy to manipulate, clean, and analyze structured datasets efficiently.
Perform Data Cleaning and Transformation: Learn techniques to handle missing data, remove duplicates, and prepare datasets for accurate analysis.
Master Data Visualization and Dashboard Creation: Create professional visualizations and interactive dashboards using tools like Matplotlib, Seaborn, Plotly, Streamlit, and Power BI.
Apply Statistical Analysis for Business Decisions: Use statistical methods such as descriptive statistics, probability, hypothesis testing, and regression to support decision-making.
Learn SQL for Data Extraction and Analysis: Write SQL queries to retrieve, join, and analyze data from relational databases for business insights.
Perform Exploratory Data Analysis and Generate Insights: Identify trends, patterns, and correlations to understand business performance and opportunities.
Work on Real-World Projects and Career Preparation: Gain hands-on experience with real datasets, build dashboards, and develop job-ready skills for Data Analyst and BI roles.

✅ Real-time case studies | ✅ Practical hands-on labs | ✅ Industry tools training | ✅ Certification Guidance

✅ Beginner Friendly ✅ Industry Projects ✅ Job Ready Skills

WHO SHOULD JOIN?

ASPIRING DATA ANALYSTS

Learn practical Data Analytics skills using Python, Pandas, NumPy, SQL, statistics and data visualization.

BUSINESS & REPORTING PROFESSIONALS

Ideal for professionals who want to analyze business data, create reports and dashboards, and generate data-driven insights.

IT & DATABASE PROFESSIONALS

Build hands-on expertise in SQL, Python-based data manipulation, data cleaning, exploratory analysis and database-driven analytics.

GRADUATES & CAREER SWITCHERS

Suitable for beginners, freshers and professionals looking to move into Data Analyst, Business Analyst or BI-related careers. No programming knowledge is required; foundational knowledge will be provided.

Basic computer knowledge and a laptop/desktop with at least 8 GB RAM are recommended.

CAREER OUTCOMES

Build practical skills for roles such as:

Data Analyst

Data Analysis • Data Cleaning • Visualization

Business Data Analyst

Business Insights • Statistical Analysis • Reporting

BI Analyst

Power BI • Dashboard Development • Data Visualization

Python Data Analyst

Python • Pandas • NumPy

SQL Data Analyst

SQL Queries • Data Extraction • Data Analysis

Data Visualization Specialist

Matplotlib • Seaborn • Plotly

Data Analytics Engineer

Data Transformation • EDA • Analytics Workflows

Junior Data Scientist / Analytics Professional

Statistics • Regression • Hypothesis Testing

COURSE CURRICULUM

25+ Course Topics • Hands-on Labs • Real-Time Practice

MODULE 1: FOUNDATIONS

01 Introduction to Data Analytics Topics

What is Data Analytics vs Data Science vs Business Intelligence
Roles and responsibilities: Business Analyst, Data Analyst, BI Developer
Analytics workflow: Data collection → Analysis → Visualization → Insights → Action
Real-world case studies from industry (finance, retail, e-commerce, healthcare)
Career paths and salary benchmarks in analytics

02 Python Environment Setup for Analytics Topics

Installing Python, Jupyter Notebook, VS Code
Package managers: pip, conda
Setting up virtual environments
Introduction to IDE best practices

03 Python Fundamentals for Analytics Topics

Language fundamentals: variables, data types, operators
Control flow: if-else, loops (for, while)
String manipulation and formatting
Lists, tuples, dictionaries for data handling
Functions: defining, parameters, return values

MODULE 2: DATA MANIPULATION & EXPLORATION

04 NumPy Essentials Topics

NumPy arrays vs Python lists: performance benefits
Array creation, indexing, slicing
Mathematical operations on arrays
Broadcasting concepts
Statistical functions (mean, median, std, percentile)
Practical: Working with multi-dimensional datasets

05 Pandas for Data Analysis (Core Module) Topics

Series and DataFrame fundamentals
Creating DataFrames from CSV, Excel, SQL databases
Indexing and selecting data
Viewing and inspecting data (head, tail, info, describe)

06 Data Cleaning and Transformation Topics

Handling missing values (dropna, fillna, interpolation)
Removing duplicates
Data type conversions
Handling outliers

07 Data Manipulation Topics

Filtering and sorting
Groupby operations and aggregations
Pivot tables and crosstabs
Merging, joining, concatenating DataFrame

08 Time Series Basics Topics

DateTime handling
Resampling and rolling windows
Trend analysis

09 Exploratory Data Analysis (EDA) Theory Topics

Understanding data distribution
Identifying patterns and relationships
Statistical thinking for analysts
Types of analysis: univariate, bivariate, multivariate
Hypothesis formation from data exploration

MODULE 3: DATA VISUALIZATION & STORYTELLING

10 Matplotlib Fundamentals Topics

Figure and axis objects
Line plots, bar charts, histograms, scatter plots
Customization: colors, markers, line styles
Legend, labels, titles, annotations
Grid and axis formatting
Multiple plots: subplots and figure layouts

11 Advanced Visualization with Seaborn Topics

Statistical visualization concepts
Categorical plots: bar, box, violin, strip plots
Distribution plots: histograms, KDE plots
Relationship plots: scatter, regression plots, heatmaps
Color palettes and styling
FacetGrid for multi-dimensional analysis

12 Interactive Dashboards with Plotly & Streamlit Topics

Plotly basics: interactive charts and hover information
Creating dashboards with Streamlit
Deploying Streamlit apps to cloud (Streamlit Cloud, Heroku)
Real-time data updates
Dashboard best practices

13 Data Storytelling Topics

Principles of data visualization
Choosing the right chart for the message
Narrative structure: context, conflict, resolution
Presenting insights to non-technical stakeholders
Creating compelling business report

MODULE 4: STATISTICS FOR BUSINESS DECISIONS

14 Descriptive Statistics Topics

Central tendency: mean, median, mode
Spread: range, variance, standard deviation, IQR
Skewness and kurtosis
Outlier detection and handling (IQR method, Z-score)
Weighted statistics

15 Probability Basics Topics

Fundamentals: sample space, events, probability rules
Conditional probability and Bayes' theorem
Independence and dependence
Real-world application scenarios

16 Inferential Statistics Topics

Sampling concepts and distributions
Confidence intervals: for mean and proportion
Hypothesis testing: null vs alternative hypotheses
T-tests, Z-tests, chi-square tests
P-values and significance levels
Type I and Type II errors

17 Correlation and Simple Regression Topics

Correlation: Pearson, Spearman, Kendall
Scatter plots and relationship identification
Simple linear regression: model interpretation
R² and RMSE for model evaluation
Residual analysis

18 A/B Testing for Business Topics

Experiment design and setup
Sample size calculation
Statistical significance testing
Business impact vs statistical significance
Case studies: marketing, product optimization

MODULE 5: SQL FOR DATA ANALYSTS

19 SQL Fundamentals Topics

SELECT, WHERE, ORDER BY, LIMIT
Filtering with AND, OR, NOT, IN, BETWEEN
DISTINCT and COUNT

20 Data Aggregation & Joins Topics

GROUP BY and aggregation functions (SUM, AVG, MAX, MIN, COUNT)
HAVING clause
INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN
Self-joins and multiple joins

21 Intermediate SQL Topics

Subqueries and CTEs (Common Table Expressions)
Window functions: ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD
Date functions and time-based analysis
CASE statements for conditional logic

22 Practical Analytics Queries Topics

Customer segmentation queries
Cohort analysis
Funnel analysis
Revenue analysis and trends
Practice: Real-world dataset analysis

MODULE 6: CAPSTONE PROJECT

23 End-to-End Analytics Project Topics

Objective: Analyze a real-world dataset and present business insights
Phases:
1. Data sourcing and initial exploration
2. Data cleaning and preparation
3. EDA with statistical analysis
4. Hypothesis testing and validation
5. Creating visualization dashboard
6. Business recommendations and ROI impact
Deliverables:
Cleaned dataset with documentation
Jupyter notebook with analysis
Interactive dashboard (Streamlit or Plotly)
Executive summary report
Presentation to stakeholders
Example Projects:
Customer retention analysis (e-commerce)
Sales trends and forecasting (retail)
User behavior analysis (SaaS platform)
Healthcare outcome analysis

24 TOOLS & TECHNOLOGIES USED Topics

Languages: Python 3.9+
Data Manipulation: Pandas, NumPy
Visualization: Matplotlib, Seaborn, Plotly, Streamlit
Databases: SQL (MySQL, PostgreSQL)
Notebooks: Jupyter, Google Colab
Deployment: Streamlit Cloud, GitHub

25 POWER BI TOOLS and PROJECTS Topics

POWER BI TOOLS and PROJECTS

HANDS-ON LABS YOU WILL WORK ON

Python Programming for Data Analysis

NumPy & Pandas Data Manipulation

Data Cleaning & Transformation

Exploratory Data Analysis (EDA)

Data Visualization with Matplotlib & Seaborn

SQL Queries & Analytics

Statistics, Correlation & Regression

Power BI & End-to-End Analytics Project

24×7 Access to Dedicated Cloud Lab

MEET YOUR TRAINER

Surya Kumar

Data Science Professional

15+ Years of Experience

Senior AI Engineer | Agentic & Generative AI | AWS Cloud | PMP Certified

With 12 years of industry experience, I specialize in deploying AI-powered solutions at scale, driving digital transformation for diverse organizations. My core expertise spans large language models (LLMs), advanced machine learning pipelines, retrieval-augmented generation (RAG), and AI-driven feature engineering.

I bring hands-on experience with full-stack solution development, cloud-native AI deployments on AWS, and proven cross-functional delivery in both startup and enterprise settings. My project portfolio includes designing and implementing state-of-the-art generative AI applications, optimizing ML workflows for production, and leading teams in complex, agentic AI integrations.

As a certified Project Management Professional (PMP), I ensure technical excellence and timely project execution. My training approach blends deep technical knowledge with practical, real-world application—empowering teams to deliver robust and innovative AI systems.

Key Skills:

Generative AI & LLMs (OpenAI, HuggingFace, RAG)
ML Pipeline Design & Feature Engineering
Cloud Deployment (AWS EC2, SageMaker, Lambda, API Gateway)
Full-stack Application Development (Python, JavaScript, REST APIs)
Project Leadership (Agile, Cross-functional Teams, PMP Certified)
AI Solution Architecture & Productionization
Technical Training & Mentorship

Oracle DBA RAC Data Guard ASM Performance Cloud

STUDENT SUCCESS STORIES

Student Student

★★★★★

4405 learner reviews are currently available for this course.

LEARNING MODES

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Real-time doubt clearing
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FAQs

Each of our live sessions are recorded. In case if you miss any, you can request us to share the link of that particular session.

Once you get registered, our back-end team will share you the details to join the session live over online portal which can be accessed through a browser.

For practical execution, our trainer/technical team will provide server access details to the student.

Yes. We do provide the step-by-step document which you can follow and if required our technical team will assist you.

Live-Online training is where you can have live session with the trainer and clarify queries parallely.

Pre-recorded sessions are the recorded videos that will be provided to you that you can see, listen and learn anytime at ur feasible place. For doubts in the videos you can mail the trainer regarding the same.

You can contact our support team, or just drop an email to online@akswave-trainings.com with your queries.

The course material and recorded videos which are provided during the course period. You can download it anytime.

Visit our website regularly to check discount offers time to time. However, we provide discount for single participant & special discount for 2 or more participants.

* If request for cancellation is made within 2 days of enrolment for class, 100% refunded.

* If request made after 2 days, then Refund is made after deduction of administration fee.

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DATA Analyst

COURSE OVERVIEW

WHO SHOULD JOIN?

CAREER OUTCOMES

PRACTICE LIKE A REAL PROFESSIONAL

What You Will Learn to Do in Real-World Projects

Python for Data Analysis

NumPy & Pandas Data Manipulation

Data Cleaning & Transformation

Exploratory Data Analysis

Data Visualization & Storytelling

Statistics for Business Decisions

SQL Data Analysis

Interactive Dashboards & Projects

COURSE CURRICULUM

HANDS-ON LABS YOU WILL WORK ON

Python Programming for Data Analysis

NumPy & Pandas Data Manipulation

Data Cleaning & Transformation

Exploratory Data Analysis (EDA)

Data Visualization with Matplotlib & Seaborn

SQL Queries & Analytics

Statistics, Correlation & Regression

Power BI & End-to-End Analytics Project

WHAT'S INCLUDED IN THIS PROGRAM

40 Live Sessions

70% Hands-On Training

100+ hrs Lab Practices

24×7 Lab Access

Resume Building

Job Support

Real-World Projects

Internship Opportunities

Technical Mentorship

Career Guidance

GitHub Portfolio

Interview Preparation

Course Completion Certificate

MEET YOUR TRAINER

Surya Kumar

STUDENT SUCCESS STORIES

PLACEMENT ASSISTANCE

ResumePreparation

MockInterviews

TechnicalInterview Q&A

CompanyReferrals

PlacementSupport

UPCOMING BATCHES

LEARNING MODES

CLASSROOMTRAINING

LIVE ONLINETRAINING

SELF-PACEDLEARNING

FAQs

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Mock
Interviews

Technical
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Referrals

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