Mastering Data Analytics for Career Excellence

Achieving Excellence in Data Analytics for Career Advancement

Category Skill Development

Mastering Data Analytics for Career Excellence

What you'll learn

Master Data Analysis Techniques: Learn to clean, preprocess, and analyze data using tools like Python, SQL, and Excel for actionable insights.
Create Impactful Visualizations: Build interactive dashboards and visual reports using Power BI and Tableau to effectively communicate data stories.
Apply Statistical Methods: Understand key statistical concepts and hypothesis testing to make data-driven decisions.
Develop Predictive Models: Use machine learning algorithms and time series forecasting to predict trends and solve business problems.
Build a Portfolio for Career Growth: Work on real-world projects, develop a strong portfolio, and gain job-ready skills for data-driven roles.

Course Syllabus

Module 1: Introduction to Data Analytics

Diagnostic
Predictive and Prescriptive Analytics.";i:2;s:81:"3. Tools of the Trade:- Introduction to Excel
Python
SQL
Tableau and Power BI.";i:3;s:75:"4. Real-World Use Cases:- Case studies from healthcare
analysis
and decision-making.";i:1;s:87:"2. Types of Analytics:- Descriptive
cleaning
retail and finance.";i:4;s:94:"5. Hands-On Activity:- Identify types of analytics from a given dataset and propose solutions.";}
visualization
a:5:{i:0;s:120:"1. Understanding the Data Analytics Lifecycle - Data collection

Module 2: Data Wrangling and Preprocessing

Excel (filters
NumPy)
and inconsistent data.";i:1;s:86:"2. Feature Engineering:- Encoding categorical variables
conditional formatting).";i:4;s:79:"5. Hands-On Activity: - Clean and preprocess a messy dataset for analysis.";}
correlations and trends.";i:3;s:90:"4. Tools and Techniques:- Python (Pandas
duplicate
scaling and normalizing data.";i:2;s:90:"3. Exploratory Data Analysis (EDA):- Understanding distributions
a:5:{i:0;s:81:"1. Data Cleaning Techniques:- Handling missing

Module 3: Statistical Foundations

ANOVA and confidence intervals.";i:3;s:63:"4. Data Distributions:- Normal binomial
Poisson distributions.";i:4;s:86:"5. Hands-On Activity: - Perform hypothesis testing on a business problem dataset.";}
and Bayes' theorem.";i:2;s:82:"3. Hypothesis Testing:- t-tests
and standard deviation.";i:1;s:93:"2. Probability Concepts:- Basics of probability
chi-square tests
conditional probability
median
mode
variance
a:5:{i:0;s:103:"1. Measures of Central Tendency and Variability:- Mean

Module 4: Advanced Excel for Data Analysis

conditional formatting and sparklines.";i:1;s:77:"2. Pivot Tables and Slicers: - Advanced analysis and dashboard creation.";i:2;s:74:"3. Excel Automation with VBA: - Creating macros for repetitive tasks.";i:3;s:79:"4. Hands-On Activity: - Build an interactive dashboard for sales analysis.";}
a:4:{i:0;s:79:"1. Data Visualization in Excel:- Charts

Module 5: Python for Data Analytics

visualize key metrics and create a summary report.";}
a:5:{i:0;s:43:"1. Data Manipulation with Pandas and NumPy.";i:1;s:50:"2. Data Visualization with Matplotlib and Seaborn.";i:2;s:40:"3. Introduction to Time Series Analysis.";i:3;s:48:"4. Automating Routine Tasks with Python Scripts.";i:4;s:92:"5. Hands-On Activity:- Analyze a dataset

Module 6: SQL for Data Analytics

WHERE and GROUP BY clauses.";i:1;s:68:"2. Advanced SQL Techniques:- Joins
performance tuning and normalization.";i:3;s:79:"4. Hands-On Activity: - Query a large dataset to find actionable insights.";}
subqueries and window functions.";i:2;s:72:"3. Database Management:- Indexing
a:4:{i:0;s:66:"1. Writing Basic SQL Queries:- SELECT

Module 7: BI Tools and Dashboarding

tree maps and drill-throughs.";i:3;s:76:"4. Storytelling with Data:- Designing effective dashboards for stakeholders.";i:4;s:74:"5. Hands-On Activity:- Build a comprehensive dashboard for financial data.";}
a:5:{i:0;s:45:"1. Getting Started with Power BI and Tableau.";i:1;s:31:"2. Creating Dynamic Dashboards.";i:2;s:78:"3. Advanced Visualization Techniques:- Heatmaps

Module 8: Predictive Analytics

SARIMA and seasonal decomposition.";i:4;s:79:"5. Hands-On Activity: - Predict sales performance using regression models.";}
random forests and SVMs.";i:3;s:70:"4. Time Series Forecasting:- ARIMA
a:5:{i:0;s:89:"1. Introduction to Machine Learning for Analytics:- Supervised vs. unsupervised learning.";i:1;s:58:"2. Regression Techniques:- Linear and logistic regression.";i:2;s:71:"3. Classification Techniques:- Decision trees

Module 9: Real-World Applications

a:5:{i:0;s:73:"1. Marketing Analytics:- Campaign optimization and customer segmentation.";i:1;s:66:"2. Financial Analytics:- Fraud detection and credit risk analysis.";i:2;s:53:"3. Operational Analytics:- Supply chain optimization.";i:3;s:74:"4. Case Studies:- Work on a complete project from data to decision-making.";i:4;s:75:"5. Hands-On Activity:- Solve a business problem from a provided case study.";}

Module 10: Portfolio Building and Career Preparation

Kaggle).";i:1;s:63:"2. Resume Building:- Highlighting relevant skills and projects.";i:2;s:63:"3. Mock Interviews:- Practice data-related interview questions.";i:3;s:76:"4. Networking and Job Search Tips:- Leveraging LinkedIn and industry events.";i:4;s:78:"5. Hands-On Activity:- Create a GitHub portfolio with at least three projects.";}
a:5:{i:0;s:74:"1. Portfolio Development:- Creating project repositories (GitHub

Capstone Project

analysis
including data cleaning
visualization and creating a final report/dashboard for a business problem.";}
a:1:{i:0;s:156:"Learners will complete an end-to-end project

Course Syllabus

1. Understanding the Data Analytics Lifecycle - Data collection, cleaning, analysis, visualization, and decision-making.
2. Types of Analytics:- Descriptive, Diagnostic, Predictive and Prescriptive Analytics.
3. Tools of the Trade:- Introduction to Excel, Python, SQL, Tableau and Power BI.
4. Real-World Use Cases:- Case studies from healthcare, retail and finance.
5. Hands-On Activity:- Identify types of analytics from a given dataset and propose solutions.

1. Data Cleaning Techniques:- Handling missing, duplicate, and inconsistent data.
2. Feature Engineering:- Encoding categorical variables, scaling and normalizing data.
3. Exploratory Data Analysis (EDA):- Understanding distributions, correlations and trends.
4. Tools and Techniques:- Python (Pandas, NumPy), Excel (filters, conditional formatting).
5. Hands-On Activity: - Clean and preprocess a messy dataset for analysis.

1. Measures of Central Tendency and Variability:- Mean, median, mode, variance, and standard deviation.
2. Probability Concepts:- Basics of probability, conditional probability, and Bayes' theorem.
3. Hypothesis Testing:- t-tests, chi-square tests, ANOVA and confidence intervals.
4. Data Distributions:- Normal binomial, Poisson distributions.
5. Hands-On Activity: - Perform hypothesis testing on a business problem dataset.

1. Data Visualization in Excel:- Charts, conditional formatting and sparklines.
2. Pivot Tables and Slicers: - Advanced analysis and dashboard creation.
3. Excel Automation with VBA: - Creating macros for repetitive tasks.
4. Hands-On Activity: - Build an interactive dashboard for sales analysis.

1. Data Manipulation with Pandas and NumPy.
2. Data Visualization with Matplotlib and Seaborn.
3. Introduction to Time Series Analysis.
4. Automating Routine Tasks with Python Scripts.
5. Hands-On Activity:- Analyze a dataset, visualize key metrics and create a summary report.

1. Writing Basic SQL Queries:- SELECT, WHERE and GROUP BY clauses.
2. Advanced SQL Techniques:- Joins, subqueries and window functions.
3. Database Management:- Indexing, performance tuning and normalization.
4. Hands-On Activity: - Query a large dataset to find actionable insights.

1. Getting Started with Power BI and Tableau.
2. Creating Dynamic Dashboards.
3. Advanced Visualization Techniques:- Heatmaps, tree maps and drill-throughs.
4. Storytelling with Data:- Designing effective dashboards for stakeholders.
5. Hands-On Activity:- Build a comprehensive dashboard for financial data.

1. Introduction to Machine Learning for Analytics:- Supervised vs. unsupervised learning.
2. Regression Techniques:- Linear and logistic regression.
3. Classification Techniques:- Decision trees, random forests and SVMs.
4. Time Series Forecasting:- ARIMA, SARIMA and seasonal decomposition.
5. Hands-On Activity: - Predict sales performance using regression models.

1. Marketing Analytics:- Campaign optimization and customer segmentation.
2. Financial Analytics:- Fraud detection and credit risk analysis.
3. Operational Analytics:- Supply chain optimization.
4. Case Studies:- Work on a complete project from data to decision-making.
5. Hands-On Activity:- Solve a business problem from a provided case study.

1. Portfolio Development:- Creating project repositories (GitHub, Kaggle).
2. Resume Building:- Highlighting relevant skills and projects.
3. Mock Interviews:- Practice data-related interview questions.
4. Networking and Job Search Tips:- Leveraging LinkedIn and industry events.
5. Hands-On Activity:- Create a GitHub portfolio with at least three projects.

Learners will complete an end-to-end project, including data cleaning, analysis, visualization and creating a final report/dashboard for a business problem.

Requirements

Laptop
Good wifi

Description

Mastering Data Analytics for Career Excellence

The "Mastering Data Analytics for Career Excellence" course is a comprehensive program designed to equip learners with the skills needed to excel in data-driven roles.

Who this course is for:

Covering the entire analytics lifecycle, the course delves into advanced techniques for data cleaning, statistical analysis, and visualization using industry-standard tools like Excel, Python, SQL, Power BI, and Tableau. Learners will work on real-world d
With a strong emphasis on hands-on practice, portfolio building, and career preparation, this course is tailored for professionals and enthusiasts seeking to transition into or advance in the field of data analytics.
With a strong emphasis on hands-on practice, portfolio building, and career preparation, this course is tailored for professionals and enthusiasts seeking to transition into or advance in the field of data analytics.

Meet your instructors

Rashmi Ranjan Mangaraj

AI & Data Sceince

View Profile

Rashmi Ranjan Mangaraj is a highly skilled AI/ML Engineer with 6 years of practical, hands-on experience in developing and deploying artificial intelligence and machine learning solutions. His expertise lies in creating data-driven models and AI-powered solutions tailored to solve complex business c...

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Course Syllabus

Module 1: Introduction to Data Analytics

Diagnostic
Predictive and Prescriptive Analytics.";i:2;s:81:"3. Tools of the Trade:- Introduction to Excel
Python
SQL
Tableau and Power BI.";i:3;s:75:"4. Real-World Use Cases:- Case studies from healthcare
analysis
and decision-making.";i:1;s:87:"2. Types of Analytics:- Descriptive
cleaning
retail and finance.";i:4;s:94:"5. Hands-On Activity:- Identify types of analytics from a given dataset and propose solutions.";}
visualization
a:5:{i:0;s:120:"1. Understanding the Data Analytics Lifecycle - Data collection

Module 2: Data Wrangling and Preprocessing

Excel (filters
NumPy)
and inconsistent data.";i:1;s:86:"2. Feature Engineering:- Encoding categorical variables
conditional formatting).";i:4;s:79:"5. Hands-On Activity: - Clean and preprocess a messy dataset for analysis.";}
correlations and trends.";i:3;s:90:"4. Tools and Techniques:- Python (Pandas
duplicate
scaling and normalizing data.";i:2;s:90:"3. Exploratory Data Analysis (EDA):- Understanding distributions
a:5:{i:0;s:81:"1. Data Cleaning Techniques:- Handling missing

Module 3: Statistical Foundations

ANOVA and confidence intervals.";i:3;s:63:"4. Data Distributions:- Normal binomial
Poisson distributions.";i:4;s:86:"5. Hands-On Activity: - Perform hypothesis testing on a business problem dataset.";}
and Bayes' theorem.";i:2;s:82:"3. Hypothesis Testing:- t-tests
and standard deviation.";i:1;s:93:"2. Probability Concepts:- Basics of probability
chi-square tests
conditional probability
median
mode
variance
a:5:{i:0;s:103:"1. Measures of Central Tendency and Variability:- Mean

Module 4: Advanced Excel for Data Analysis

conditional formatting and sparklines.";i:1;s:77:"2. Pivot Tables and Slicers: - Advanced analysis and dashboard creation.";i:2;s:74:"3. Excel Automation with VBA: - Creating macros for repetitive tasks.";i:3;s:79:"4. Hands-On Activity: - Build an interactive dashboard for sales analysis.";}
a:4:{i:0;s:79:"1. Data Visualization in Excel:- Charts

Module 5: Python for Data Analytics

visualize key metrics and create a summary report.";}
a:5:{i:0;s:43:"1. Data Manipulation with Pandas and NumPy.";i:1;s:50:"2. Data Visualization with Matplotlib and Seaborn.";i:2;s:40:"3. Introduction to Time Series Analysis.";i:3;s:48:"4. Automating Routine Tasks with Python Scripts.";i:4;s:92:"5. Hands-On Activity:- Analyze a dataset

Module 6: SQL for Data Analytics

WHERE and GROUP BY clauses.";i:1;s:68:"2. Advanced SQL Techniques:- Joins
performance tuning and normalization.";i:3;s:79:"4. Hands-On Activity: - Query a large dataset to find actionable insights.";}
subqueries and window functions.";i:2;s:72:"3. Database Management:- Indexing
a:4:{i:0;s:66:"1. Writing Basic SQL Queries:- SELECT

Module 7: BI Tools and Dashboarding

tree maps and drill-throughs.";i:3;s:76:"4. Storytelling with Data:- Designing effective dashboards for stakeholders.";i:4;s:74:"5. Hands-On Activity:- Build a comprehensive dashboard for financial data.";}
a:5:{i:0;s:45:"1. Getting Started with Power BI and Tableau.";i:1;s:31:"2. Creating Dynamic Dashboards.";i:2;s:78:"3. Advanced Visualization Techniques:- Heatmaps

Module 8: Predictive Analytics

SARIMA and seasonal decomposition.";i:4;s:79:"5. Hands-On Activity: - Predict sales performance using regression models.";}
random forests and SVMs.";i:3;s:70:"4. Time Series Forecasting:- ARIMA
a:5:{i:0;s:89:"1. Introduction to Machine Learning for Analytics:- Supervised vs. unsupervised learning.";i:1;s:58:"2. Regression Techniques:- Linear and logistic regression.";i:2;s:71:"3. Classification Techniques:- Decision trees

Module 9: Real-World Applications

a:5:{i:0;s:73:"1. Marketing Analytics:- Campaign optimization and customer segmentation.";i:1;s:66:"2. Financial Analytics:- Fraud detection and credit risk analysis.";i:2;s:53:"3. Operational Analytics:- Supply chain optimization.";i:3;s:74:"4. Case Studies:- Work on a complete project from data to decision-making.";i:4;s:75:"5. Hands-On Activity:- Solve a business problem from a provided case study.";}

Module 10: Portfolio Building and Career Preparation

Kaggle).";i:1;s:63:"2. Resume Building:- Highlighting relevant skills and projects.";i:2;s:63:"3. Mock Interviews:- Practice data-related interview questions.";i:3;s:76:"4. Networking and Job Search Tips:- Leveraging LinkedIn and industry events.";i:4;s:78:"5. Hands-On Activity:- Create a GitHub portfolio with at least three projects.";}
a:5:{i:0;s:74:"1. Portfolio Development:- Creating project repositories (GitHub

Capstone Project

analysis
including data cleaning
visualization and creating a final report/dashboard for a business problem.";}
a:1:{i:0;s:156:"Learners will complete an end-to-end project

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