Courses Description

The course dives into the core fundamentals of Data Analytics, Data mining, Machine learning, statistics of data analysis, Business intelligence, and together with some relevant technology. It equips knowledge of theoretical, technical, and practical skills to become a Data Analyst. Timely exposure to, and practical experience in currently used software tools and environments. Enhance skills in algorithms and quantitative for analyzing and mining data and developing decision models in a broad range of application areas. Knowledge of industry-standard software like R, Python, Simul8, Tableau, and Oracle.

Course content

• Data Analytics Fundamental
  • Concept and Techniques of Data Analytics.
  • Types of data and data formats.
  • Types of tools.
  • Types of Analytics.
  • Advantage of Data Analytics.
  • Static Analysis.
  • Data understanding and Data preparation.
• Probability and Statistics for Data Analytics
  • Introduction to probability, distribution functions, and inference.
  • Different types of probability distribution functions.
  • Descriptive & Inferential Statistics.
  • Estimation and Hypothesis testing.
  • Imputation Techniques.
  • Quality analysis and Variability.
  • Data Cleaning.
  • Correlational and regression.
  • ANOVA and Chi-square
• Machine Learning
  • What is Machine Learning (ML)?
  • ML vs AI
  • Supervised Learning.
  • Machine Learning Algorithms.
  • Linear Regression.
  • Multiple Linear Regression.
  • Logistic Regression.
  • Polynomial Regression
  • Classification.
  • Clustering.
  • Introduction KNN
  • Decision Tree and boosting.
  • Neural Network.
  • Support vector classifier
  • Predictive Analytics with ML
  • Tools- Python, Matlab.
• Data Visualization
  • Concepts of Data Visualisation.
  • Principles of effective Visualisation and common Visualisation types.
  • Storytelling and decision-making.
  • Graphical Techniques.
  • Box Plot.
  • Types of Charts.
  • Evaluating an Analytics Report.
• Data Visualization Tools
  • Excel:
  • Basic to advanced Excel for Analytics.
  • Concatenate Function.
  • Data importation.
  • Text Columns.
  • Data validation.
  • Pivot Tables.
  • Filtering and Sorting.
  • Search and replace.
  • Drop-down lists.
  • Auto-fill and Flash-fill.
  • Conditional Formatting.
  • Dashboard Presentation in Excel.
  • Power Bi
  • Overview Business Intelligence.
  • Business Intelligence Vs Business Analytics.
  • Source Data connection.
  • Data Transformation.
  • Csv File.
  • Dashboard.
  • Publishing reports, creating apps, and setting up workspace.
  • DAX for calculated columns and measures.
  • Building Blocks.
  • Power BI portal.
  • Tableau
  • Introduction to Tableau Desktop
  • Interface.
  • Tableau server architecture.
  • Workbook, sheets, and dashboard.
  • Build Reports.
  • Connection to Data Source (Database Server).
  • Data Extractions.
  • Types of charts
  • Use Google Maps.
  • Storytelling with data in Tableau- create a story
  • Calculation and Filters.
  • Parameters.
  • Data cleaning.
  • Heat maps and Tree maps.
  • Data manipulation using R.
• Python in Data Analytics
  • Introduction Python programming language.
  • Data types, Variables, and expressions.
  • Classes and Object.
  • Loops and Functions.
  • Simple input and output.
  • OOP Concepts.
  • Data Flow.
  • Python NumPy Package.
  • Pandas Package
  • File Handling.
  • Data Visualisation in Python.
• SQL
  • Introduction to Relational Database.
  • Select statements.
  • DDL
  • DML
  • Filtering data using Where clause.
  • Data types.
  • Aggregated Data
  • Indexing and Query planning.
  • SQL Joins.
  • Relational and Logical Operators.
  • Using Subqueries.
  • Set operators.
  • Case Statements.
  • Procedures.
  • Data management in different time zones.
  • Manipulate large sets of Data.
• Data Mining
  • Concepts of Data Mining.
  • Data Mining Techniques for Data Analysis.
  • Pattern Mining.
  • Explore data for insight.
  • Linear Models and Generalised Linear Models (GLMs)
• Big Data
  • Big Data Theory.
  • Big Data Storage.
  • Introduction to Hadoop.
  • Apache Hadoop.
  • Big Data Technologies.
  • HDFS.
  • MapReduce.
  • Hands-on experience in tools like Hadoop Hive and Zeppelin.

Why Choose Tops Technologies ?

Highlights of Our Power BI Course

• Data analysts earn an average salary of $67,000 annually in the United States.
• The demand for data analysts is projected to grow by 31% over the next decade.
• Over 2.5 quintillion bytes of data are generated globally every day.
• Data analysts spend about 80% of their time cleaning and preparing data.
• Companies that use data-driven decision-making are 6% more profitable than those that don't.
• Data analysts typically use an average of 5 different tools or technologies to perform their jobs.
• 87% of organizations consider data analytics a strategic priority.
• 53% of companies have adopted big data analytics, up from 17% in 2015.