Data Analytics

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About Course

This Data Analytics Course will equip you with in-demand skills in Excel, SQL, Python, Tableau, and Machine Learning to analyze, visualize, and make data-driven decisions. Whether you’re an aspiring analyst, business professional, or tech enthusiast, this course provides hands-on projects, real-world case studies, and expert guidance to help you master data analytics from foundations to advanced techniques. Don’t just consume data—start leveraging it for success today.

What Will You Learn?

  • Fundamentals of Data Analytics
  • Data Collection, Cleaning & Preparation
  • Excel & SQL for Data Manipulation
  • Python for Data Analytics & Automation
  • Data Visualization & Business Intelligence
  • Statistics & Probability for Data Analysis
  • Machine Learning for Data Analytics
  • Big Data & Cloud Analytics
  • Data Ethics, Privacy & Compliance
  • Monetizing Data Analytics & Real-World Impact
  • Capstone Project & Career Readiness

Course Content

Introduction to Data Analytics
This module provides a foundational understanding of data analytics, covering its core concepts, real-world applications, and industry significance. Students will explore different types of data, the data analytics lifecycle, and key tools used in the field. The module also highlights career opportunities and the role of data-driven decision-making in various industries.

  • Understanding Data Analytics – Scope & Applications
  • Types of Data – Structured vs. Unstructured
  • Data Analytics Lifecycle & Workflow
  • Data-Driven Decision Making
  • Career Paths in Data Analytics

Data Collection & Cleaning
This module covers essential techniques for gathering and preparing data for analysis. Students will learn about data sources (databases, APIs, web scraping), data collection methods, and handling structured vs. unstructured data. It also focuses on data cleaning techniques, including handling missing values, removing duplicates, detecting outliers, and standardizing data formats to ensure accuracy and reliability in analysis.

Excel & SQL for Data Analytics
This module equips students with essential data manipulation and analysis skills using Excel and SQL. Students will learn Excel functions, pivot tables, data visualization, and automation techniques for handling datasets. The SQL section covers database fundamentals, writing queries, filtering and aggregating data, joins, subqueries, and advanced SQL functions, enabling efficient data extraction and analysis from relational databases.

Python for Data Analytics
This module introduces Python as a powerful tool for data analysis, covering data manipulation, visualization, and automation. Students will learn to use Pandas and NumPy for handling datasets, Matplotlib and Seaborn for creating insightful visualizations, and data processing techniques for cleaning and transforming raw data. Through hands-on projects, learners will develop practical coding skills essential for modern data analytics.

Data Visualization & Business Intelligence (BI)
This module focuses on transforming raw data into meaningful insights through visual storytelling and business intelligence tools. Students will learn to create effective charts, graphs, and dashboards using Tableau, Power BI, and Python libraries (Matplotlib, Seaborn, Plotly). The module also covers data-driven decision-making, report building, and interactive dashboard development to help businesses make strategic, data-backed decisions.

Statistics & Probability for Data Analysis
This module provides a strong mathematical foundation for data analysis by covering descriptive and inferential statistics, probability distributions, and hypothesis testing. Students will learn key concepts such as mean, median, variance, standard deviation, correlation, regression, and A/B testing to interpret data patterns and make data-driven predictions. Practical applications and real-world case studies will reinforce the use of statistics in decision-making.

Machine Learning for Data Analytics
This module introduces machine learning techniques used in data analytics, covering both supervised and unsupervised learning. Students will learn to apply regression, classification, clustering, and dimensionality reduction using Python libraries like Scikit-Learn and TensorFlow. The module also focuses on model evaluation, feature engineering, and predictive analytics, enabling learners to build data-driven models for real-world business insights.

Big Data & Cloud Analytics
This module explores handling and analyzing large-scale datasets using Big Data technologies and cloud platforms. Students will learn about Hadoop, Spark, and distributed computing for processing massive data volumes. The module also covers cloud-based analytics using AWS, Google Cloud, and Azure, including data storage, real-time processing, and scalable analytics solutions to derive meaningful business insights.

Data Ethics, Privacy & Compliance
This module covers the ethical, legal, and regulatory aspects of data analytics, focusing on responsible data handling, user privacy, and compliance standards. Students will explore topics such as bias in data, data protection laws (GDPR, CCPA), ethical AI, and cybersecurity best practices. The module emphasizes the importance of transparent, fair, and secure data usage in today's digital landscape.

Monetizing Data Analytics & Real-World Impact
This module explores practical ways to apply data analytics skills for financial success and societal impact. Students will learn how to monetize their expertise through freelancing, consulting, entrepreneurship, and high-paying career paths. It also covers leveraging data for business growth, financial markets, and product development, while emphasizing networking, personal branding, and ethical considerations to maximize opportunities in the data-driven economy.

Capstone Project & Career Readiness
This module allows students to apply their data analytics skills to a real-world project, solving a business problem using data-driven insights. They will work with large datasets, apply machine learning models, and create interactive dashboards. Additionally, the module includes career preparation, covering resume building, portfolio development, interview preparation, and networking strategies to help students transition into the data analytics industry.

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