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Data Analytics

Data analytics is the process of examining, organizing, and interpreting data to uncover patterns, insights, and trends, helping businesses make informed decisions and improve performance.

Lesson 1: Introduction to Data Analytics

  1. What is Data Analytics? - Definition, Importance in Decision-Making, Applications in Various Industries

  2. Types of Data Analytics - Descriptive Analytics, Diagnostic Analytics, Predictive Analytics, Prescriptive Analytics

  3. The Data Analytics Process - Data Collection, Data Cleaning, Data Analysis, Data Visualization

Lesson 2: Understanding Data

  1. Data Basics - Structured vs Unstructured Data, Qualitative vs Quantitative Data

  2. Sources of Data - Databases, APIs, Web Scraping

  3. Characteristics of Quality Data - Accuracy, Completeness, Consistency

Lesson 3: Statistics for Data Analytics

  1. Descriptive Statistics - Mean, Median, Mode, Standard Deviation, Variance, Correlation, Covariance

  2. Inferential Statistics - Hypothesis Testing, Confidence Intervals, p-Value, Statistical Significance

Lesson 1: Data Wrangling

  1. Data Cleaning Techniques - Handling Missing Values, Removing Duplicates, Data Standardization

  2. Data Transformation - Normalization and Scaling, Encoding Categorical Variables

Lesson 2: Data Visualization

  1. Principles of Effective Visualization - Choosing the Right Chart, Avoiding Misleading Visuals

  2. Visualization Tools - Tableau, Power BI, Matplotlib, Seaborn (Python Libraries)

Lesson 3: Data Analytics Tools

  1. Spreadsheet Software - Excel: Pivot Tables, Formulas

  2. Programming Languages - Python: Pandas, NumPy, R: dplyr, ggplot2

  3. Databases - SQL Basics: Select, Join, Aggregate

Lesson 1: Predictive Analytics

  1. Introduction to Machine Learning - Regression Models, Classification Models, Decision Trees

  2. Applications of Predictive Analytics - Sales Forecasting, Customer Segmentation

Lesson 2: Big Data Analytics

  1. Big Data Fundamentals - Characteristics of Big Data (Volume, Variety, Velocity), Big Data Tools: Hadoop, Spark

  2. Processing Big Data - Distributed Computing, MapReduce

Lesson 3: Data Storytelling and Communication

  1. Communicating Insights - Creating Dashboards, Presenting Insights Clearly

  2. Best Practices in Data Storytelling - Focusing on the Audience, Highlighting Key Takeaways

Data Analytics
  • CategoryData Science
  • LevelIntermediate
  • Duration3 Months
  • Available SeatsUnlimited

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Flexible learning options to assist you in every way possible.

No Cost EMI

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Develop essential soft skills along with technical knowledge.

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