Data Analytics

Data Analytics Course

Course Description

The Data Analytics Professional Certificate Course with IBM Certification is designed to help students and professionals build strong analytical thinking and data interpretation skills. In today’s data-first business environment, this course equips you to gather, clean, analyse, and present data effectively—making you a valuable asset in any industry.

With hands-on practice and real-world datasets, you’ll learn how to use tools like Python, R Programming, Tableau, Excel, SQL, and Power BI to uncover insights and support business decisions. This course is perfect for anyone looking to start or grow a career in data analysis, with no prior experience required.

What You’ll Learn From This Course

Analyse and visualize data using Excel, SQL, Tableau, and Power BI
Write data scripts and perform analysis using Python and R
Clean, transform, and interpret data using real-world datasets
Understand concepts like KPIs, data modelling, business metrics, and forecasting
Gain hands-on project experience and receive a globally recognized certificate from IBM

By the end of this course, you’ll be ready to handle real-world data problems confidently and communicate insights that drive decisions. With dual certification from IBM and ArivuPro Skills, your resume stands out in today’s competitive job market.

Curriculum

36 Sections
120 Lessons
150 Hours

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Excel with Automation (Part 1)
0
Module 1: Data Management in Excel
4
- 2.1
  Data Cleaning and Preparation: Removing duplicates, handling missing data.
- 2.2
  Managing Large Datasets: Using Freeze Panes, Filters, and Excel Tables.
- 2.3
  Advanced Filtering and Sorting: Custom sorting and filtering techniques.
- 2.4
  Using Excel Tables: Structured references and data organization.
Module 2: Data Analysis and Charting
4
- 3.1
  PivotTables and Pivot Charts: Creating and customizing PivotTables and charts.
- 3.2
  Slicers and Timelines: Dynamic filtering of PivotTables and charts.
- 3.3
  Advanced Charting: Creating Waterfall, Funnel, and Combo charts.
- 3.4
  Conditional Formatting: Highlighting data patterns with color scales and icons.
Module 3: Excel Formulas
4
- 4.1
  Logical Formulas: IF, AND, OR for decision-making.
- 4.2
  Lookup and Reference: VLOOKUP, INDEX, MATCH, XLOOKUP.
- 4.3
  Mathematical and Statistical: SUM, AVERAGE, COUNTIF, STDEV.
- 4.4
  Text and Date Formulas: CONCATENATE, LEFT, RIGHT, DATE, NOW.
Module 4: Data Validation and What-If Analysis
4
- 5.1
  Data Validation: Creating drop-down lists and setting entry rules.
- 5.2
  Named Ranges: Simplifying formulas and improving workbook clarity.
- 5.3
  What-If Analysis: Using Data Tables for scenario analysis.
- 5.4
  Scenario Manager and Goal Seek: Managing scenarios and reverse calculations.
Module 5: Dashboard Creation and Consolidation
4
- 6.1
  Data Consolidation: Merging data from multiple sources.
- 6.2
  Dashboard Creation: Building interactive dashboards with PivotTables and charts.
- 6.3
  Advanced Excel Functions: Integrating formulas and charts in dashboards.
- 6.4
  Final Project: Creating a complete dashboard using learned skills.
Power BI (Part 2)
0
Module 1: Introduction to Power BI
4
- 8.1
  Getting Started with Power BI Desktop and its interface.
- 8.2
  Importing and transforming data from different sources.
- 8.3
  Basic Data Modeling: Creating relationships and calculated columns.
- 8.4
  Creating basic visualizations and publishing reports to Power BI Service.
Module 2: Data Transformation and Advanced Modeling
4
- 9.1
  Advanced Data Transformation techniques using Power Query Editor.
- 9.2
  Using DAX for creating calculated columns, measures, and advanced calculations.
- 9.3
  Working with Time Intelligence functions.
- 9.4
  Building complex relationships and optimizing data models for performance.
Module 3: Visualizations and Reports
4
- 10.1
  Creating advanced visualizations: bar charts, line charts, pie charts, and combo charts.
- 10.2
  Customizing visualizations for interactive reports and dashboards.
- 10.3
  Using slicers, filters, and advanced formatting techniques.
- 10.4
  Creating reports with multiple pages and interactive features.
Module 4: Power BI Service and Sharing
4
- 11.1
  Publishing reports and dashboards to Power BI Service.
- 11.2
  Sharing and collaborating on Power BI reports with stakeholders.
- 11.3
  Setting up scheduled data refresh and report distribution.
- 11.4
  Creating workspaces and managing security permissions in Power BI Service.
Module 5: Advanced Power BI Features and Case Studies
4
- 12.1
  Integrating Power BI with Excel and other data sources.
- 12.2
  Using Power BI API for custom automation and embedding reports in applications.
- 12.3
  Advanced visualizations using custom visuals in Power BI.
- 12.4
  Hands-on project and case studies to build a complete Power BI solution.
Tableau (Part 3)
This Tableau course provides a comprehensive understanding of data visualization, from basic visualizations to advanced analytics, including data blending and storytelling techniques. Students will also learn to collaborate and manage Tableau workbooks effectively using Tableau Server.
0
Module 1: Introduction to Tableau and Data Visualization Basics
4
- 14.1
  Overview of Tableau, Tableau Desktop, and Tableau Server.
- 14.2
  Understanding the importance of data visualization in business analytics.
- 14.3
  Connecting to data sources like Excel, CSV, and databases.
- 14.4
  Creating basic visualizations such as bar charts, line graphs, and scatter plots.
Module 2: Intermediate Data Visualization Techniques
4
- 15.1
  Introduction to advanced chart types: Heatmaps, box plots, and treemaps.
- 15.2
  Customizing visualizations with colors, labels, and annotations.
- 15.3
  Understanding data blending and joins to combine multiple data sources.
- 15.4
  Creating interactive dashboards using actions, filters, and design best practices.
Module 3: Advanced Analytics with Tableau
4
- 16.1
  Working with calculated fields and parameters, including advanced calculations (IF statements, LOD expressions).
- 16.2
  Implementing forecasting models and analyzing trends in data.
- 16.3
  Visualizing spatial data through geographic mapping and customizing maps.
- 16.4
  Exploring advanced analytics such as seasonality and identifying trends in Tableau.
Module 4: Data Storytelling and Presentation Techniques
4
- 17.1
  Understanding the principles of data storytelling and structuring your narrative.
- 17.2
  Incorporating storytelling into data visualization with story points and annotations.
- 17.3
  Effective communication techniques for presenting insights to non-technical stakeholders.
- 17.4
  Presenting data findings confidently and clearly using Tableau’s visualization features.
Module 5: Tableau Server and Collaboration
4
- 18.1
  Introduction to Tableau Server and its components.
- 18.2
  Publishing and sharing Tableau workbooks on Tableau Server.
- 18.3
  Managing users and permissions, setting access controls for data security.
- 18.4
  Collaboration features such as comments, subscriptions, and alerts for effective teamwork.
Python (Part 4)
0
Module 1: Python Basics and Data Structures
4
- 20.1
  Input and output operations, variables, rules for naming variables, data types, and type conversion.
- 20.2
  What is Python, why to learn Python, and introduction to Jupyter Notebook, including its installation.
- 20.3
  Operators in Python and performing various string operations.
- 20.4
  Exploring lists, tuples, sets, dictionaries, and their respective operations.
Module 2: Control Flow, Functions, and Functional Programming
4
- 21.1
  Introduction to conditional statements and nested conditional statements.
- 21.2
  Introduction to loops, including for loop, while loop, and nested loops.
- 21.3
  List comprehensions and defining and using functions with function arguments.
- 21.4
  Functional programming concepts, including map, filter, and reduce functions.
Module 3: Numpy Array Basics
4
- 22.1
  Understanding Numpy arrays and their importance, along with the differences between lists and Numpy arrays.
- 22.2
  Creation of arrays, array attributes, indexing, and slicing operations.
- 22.3
  Performing basic operations on Numpy arrays, including shape and reshape techniques.
- 22.4
  Exploring array aggregation, stacking, and splitting.
Module 4: Pandas
4
- 23.1
  Introduction to Pandas, Series, DataFrames, creating and indexing DataFrames.
- 23.2
  DataFrame properties (shape, head, tail), reading files (CSV, Excel, JSON), and basic exploration (describe, isnull, dtypes).
- 23.3
  Statistical functions (count, mean, max, min), sorting, concatenation, handling duplicates, and managing indexes.
- 23.4
  Advanced operations: map, replace, apply, groupby, value_counts, crosstab, pivot_table, and handling missing data (dropna, fillna).
Module 5: Visualization Using Matplotlib and Seaborn
4
- 24.1
  Introduction to Matplotlib and Seaborn, creating line graphs, and adding titles, labels, colors, and styles to graphs
- 24.2
  Creating bar charts, including multiple bar charts, horizontal bar graphs, grouped bar charts, and stacked bar graphs.
- 24.3
  Plotting pie charts, scatter plots, histograms, and Seaborn visualizations like distplot, pairplot, and violinplot.
- 24.4
  Exploring advanced Seaborn plots like swarmplot, stripplot, and countplot for detailed data visualization.
R programming ( Part 5)
0
Module 1: Introduction to R and R Data Structures
4
- 26.1
  Overview of R and its applications in data analytics
- 26.2
  Installation and setup of R, RStudio, and necessary libraries
- 26.3
  Understanding basic R syntax (variables, data types, and comments)
- 26.4
  Introduction to data structures: Vectors, Lists, Matrices, Arrays, Data frames, and Factors.
Module 2: Basic Data Operations and Importing/Exporting Data (6 Hours)
4
- 27.1
  Perform basic arithmetic, logical, and vectorized operations in R
- 27.2
  Use functions and control structures (loops, if-else statements)
- 27.3
  Import data from CSV, Excel, and text files using read.csv and readxl
- 27.4
  Export data to CSV and Excel formats for further use.
Module 3: Data Visualization
4
- 28.1
  Introduction to the tidyverse package (dplyr, ggplot2, tidyr)
- 28.2
  Create basic plots using plot(), hist(), and boxplot()
- 28.3
  Use ggplot2 for advanced data visualizations (scatter plots, bar charts, histograms)
- 28.4
  Customize visualizations to present data effectively.
Module 4: Data Cleaning and Data Manipulation
4
- 29.1
  Handle missing data using functions like is.na(), na.omit(), and na.rm()
- 29.2
  Apply data imputation techniques to fill missing values
- 29.3
  Manipulate data using dplyr functions (select(), filter(), mutate(), arrange(), summarize())
- 29.4
  Join datasets with left_join(), right_join(), inner_join(), and full_join()
Module 5: Data Reshaping and Exploratory Data Analysis
4
- 30.1
  Reshape data with tidyr using unite() and separate()
- 30.2
  Identify and visualize relationships in data using correlation analysis and ggplot2
- 30.3
  Perform exploratory data analysis with descriptive statistics (mean, median, mode, standard deviation)
- 30.4
  Work with nested and hierarchical data structures
SQL (Part 6)
This SQL course covers fundamental and advanced database management techniques, including creating and modifying databases, using SQL commands, and optimizing queries. Students will gain practical skills in handling data with joins, subqueries, aggregate functions, and SQL constraints.
0
Module 1: Introduction to SQL and Basic Commands
4
- 32.1
  Introduction to DBMS, RDBMS, and SQL.
- 32.2
  Overview of SQL commands: DDL, DML, DQL, DCL, TCL.
- 32.3
  DDL commands: Create, Alter, Drop, and Truncate.
- 32.4
  DML commands: Insert, Update, Delete, and DQL: Select with WHERE clause.
Module 2: SQL Data Types, Constraints, and Operators
4
- 33.1
  Built-in data types in SQL and using the WHERE clause.
- 33.2
  IS NULL, IS NOT NULL, and compound search with WHERE commands.
- 33.3
  ALTER, DROP, Rename commands, and Modify command in SQL.
- 33.4
  Adding and dropping columns, SQL constraints, and using ALTER to add or drop constraints.
Module 3: SQL Functions and Aggregate Queries
4
- 34.1
  Using WHERE clause predicates, wildcard filtering, UNION, UNION ALL, and DISTINCT.
- 34.2
  Operator precedence in SQL and sorting query results.
- 34.3
  Numeric, string, and date functions in SQL (CAST, COALESCE).
- 34.4
  Aggregate functions: COUNT, SUM, AVG, MIN, MAX with GROUP BY and HAVING clauses.
Module 4: Joins in SQL
4
- 35.1
  Simple joins, natural joins, equi joins, non-equi joins, and self-joins.
- 35.2
  Left joins, inner joins, and outer joins.
- 35.3
  Using JOIN operations for complex queries.
- 35.4
  Understanding the significance and types of joins in relational databases.
Module 5: Subqueries and Advanced SQL Operations
4
- 36.1
  Subqueries: single-row, multiple-row, and multiple-column subqueries.
- 36.2
  Correlated subqueries and the use of EXISTS and NOT EXISTS.
- 36.3
  Practical examples and use cases of subqueries.
- 36.4
  Combining subqueries with joins for complex SQL operations.

Data Analytics

Data Analytics Course

Course Description

Curriculum