Data Analytics

Master Data Analytics Techniques

Explore the world of Data Analytics and Business Intelligence. Learn to process, analyze, and interpret data using tools like Excel, Tableau, Power BI, SQL, and Python. Our comprehensive course will equip you with the skills to make data-driven decisions.

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Comprehensive Learning

Turn Data Into Insights

From data preparation to advanced visualization, this course covers it all. Get hands-on experience with data wrangling, descriptive analytics, and creating compelling dashboards using real-world datasets.

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Project-Based Learning

Build Real-World Analytics Projects

Apply what you learn by working on real-world data analytics projects. Solve business problems, create impactful dashboards, and showcase your expertise to potential employers.

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Data Analytics Syllabus

This comprehensive 100-hour course provides in-depth knowledge of data analytics, covering the entire analytics lifecycle from data extraction to actionable insights. Below is a detailed breakdown of the syllabus:

Course Overview

This course equips participants with practical skills in data analytics, including data cleaning, visualization, statistical analysis, and dashboard creation using tools like Excel, Tableau, SQL, and Python. By the end, learners will be adept at deriving business insights from raw data.

Course Objectives

  • Understand the data analytics lifecycle and its real-world applications.
  • Learn data wrangling and visualization techniques using Python and Excel.
  • Perform statistical analysis and hypothesis testing.
  • Create interactive dashboards using Tableau and Power BI.
  • Gain proficiency in querying and managing data using SQL.

Module Breakdown

  • Introduction to Data Analytics: Definitions, trends, and use cases.
  • Excel fundamentals: Functions, formulas, and pivot tables.
  • Data cleaning techniques using Excel.
  • Case study: Analyzing business data in Excel.

  • Introduction to Tableau: Interface and basic functionalities.
  • Building visualizations: Bar charts, line charts, and scatter plots.
  • Data filtering, sorting, and grouping in Tableau.
  • Creating dashboards and stories for business insights.

  • Introduction to Python for Data Analytics.
  • Data manipulation using Pandas and NumPy.
  • Exploratory Data Analysis (EDA) with Python.
  • Case study: Preparing raw data for analysis.

  • Understanding key statistical concepts: Mean, median, variance, and standard deviation.
  • Hypothesis testing: t-tests, ANOVA, and chi-square tests.
  • Correlation and regression analysis.
  • Case study: Analyzing a business problem statistically.

  • Introduction to databases and SQL.
  • Basic queries: SELECT, WHERE, JOIN, and GROUP BY.
  • Advanced SQL: Subqueries, window functions, and stored procedures.
  • Case study: Analyzing customer data using SQL.

  • Combining all techniques learned during the course.
  • Real-world problem-solving using data analytics.
  • Presentation of findings and actionable insights.
Data Analysis Basics

Introduction to Data Analysis

Data Analysis is the process of examining, cleaning, transforming, and modeling data to discover useful information. This course covers the essentials, including understanding datasets, identifying patterns, and interpreting trends. You'll learn how to clean and organize data, perform exploratory data analysis (EDA), and summarize insights using descriptive statistics and visualizations.

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Tools and Techniques

Data Analysis Tools and Techniques

Master popular tools such as Excel, Python, and SQL for analyzing data. You'll explore techniques like pivot tables, data wrangling, and database querying. Dive into Python libraries like pandas and matplotlib for advanced data manipulation and visualization. Additionally, you'll learn statistical methods for hypothesis testing and trend analysis.

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Real-World Applications

Real-World Data Analysis Projects

Apply your skills to real-world scenarios by working on projects such as customer segmentation, sales forecasting, and web traffic analysis. Understand how to structure your findings and present them effectively using dashboards and reports. Learn how data-driven decision-making can be applied to industries like marketing, healthcare, and finance.

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FAQ

Data Analysis is the process of examining, cleaning, transforming, and interpreting data to uncover valuable insights. It involves identifying patterns, correlations, and trends in data to inform decision-making. This process can range from simple exploratory data analysis (EDA) using basic statistics to complex analyses involving advanced mathematical models. Data analysis is a critical step in many fields, including business, healthcare, marketing, and research.

Learning Data Analysis equips you with the ability to make data-driven decisions, which is a highly sought-after skill in today's data-driven world. Professionals with strong data analysis skills can solve complex problems, uncover opportunities, and drive strategic initiatives in their organizations. It opens doors to a wide range of career opportunities across various industries, including finance, healthcare, technology, and more.

Data Analysis involves a variety of tools and techniques. Commonly used tools include Microsoft Excel for basic analysis, Python and R for advanced statistical modeling, and SQL for querying databases. Visualization tools like Tableau, Power BI, and matplotlib help present data insights effectively. The choice of tools depends on the complexity of the analysis and the specific use case.