Mastering Data Analytics for Career Excellence

Achieving Excellence in Data Analytics for Career Advancement

  • Category Microsoft
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

image

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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Lead digital transformation in your organization by mastering the core concepts of generative artificial intelligence and its potential impact.

Master and integrate prompt engineering to optimize day-to-day tasks and automate workflows.

Foster an “AI friendly” culture in your organization by understanding the ethical aspects and the risks associated with the implementation of this technology.

Explore tools such as ChatGPT, as well as other emerging technologies, to improve productivity.

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Access to rich supplementary resources provides additional materials and content for a more thorough educational journey.

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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.

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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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