Unlocking Insights: Data Analysis Expertise
Gain in-demand skills to unlock insights and drive decision-making in any industry.
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Data Analytics Training in Pune
In today’s data-driven world, businesses rely heavily on insights derived from data to make informed decisions. Our Data Analytics course is designed to equip you with the analytical skills and tools necessary to interpret complex data sets and provide actionable insights. Whether you’re a beginner or a professional looking to enhance your skill set, this course will guide you through essential techniques such as data visualization, statistical analysis, and predictive modeling using the latest industry tools like Excel, Python, SQL, and Power BI.
Join us to start a rewarding career in Data Analytics, and take advantage of our hands-on projects, expert trainers, and placement assistance to help you land your dream job.
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Course highlights:
Python Programming(Basic and Advanced)
Data manipulation with pandas & numpy and Data visualisation with Tableau
Machine Learning with scikit, sklearn, scipy and statsmodels.
Web scraping and Time Series
forecasting
Text processing, NLP, Image processing with Neural Networks(ANN,RNN,CNN etc.)
Resume | Interview | Certification preparation for IABAC and IBM certification.
Syllabus
- Defining Python
- History of Python and its Growing Popularity
Features of Python and its Wide Functionality - Python 2 vs Python 3
- Setting Up Python
- Environment for Development
- What and How of Python Installation?
- IDEs: IDLE, Pycharm, and Jupyter
- Writing First Python Program
- Python Scripts on UNIX and Windows
- Installation on Ubuntu-based Machines
- Programming on Interactive Shell
- Python Identifiers and Keywords
- Indentation in Python
- Comments and Writing to the Screen
- Command Line Arguments and Flow Control
- User Input
- Python Core Objects
- Defining Built-in Functions
- Objectives
- Variables and their types
- Variables – String Variables
- Variables – Numeric Types
- Variables – Boolean Variables
- Boolean Object and None Object
- Tuple Object and Operations
- Dictionary Object and Operations
- Types of Variables – Dictionary
- Comparison of Variables
- Dictionary Methods and Manipulations
- Operators and Logical Operators
- Data Structures and Data Processing
- Arithmetic Operations on Numeric Values
- Operators and Keywords for Sequences
- Understanding Conditional Statements
- Break Statements and Continue Statements
- Using Indentations for defining if & else block
- Loops in Python
- While, Nested, Demo-Create
- How to Control Loops?
- Sequence and Iterable Objects
- Objectives of Functions
- Types of Functions
- Creating UDF Functions
- Function Parameters
- Unnamed and Named Parameters
- Creating and Calling Functions
- Python user Defined Functions
- Python packages Functions
- Anonymous Lambda Function
- Understanding String Object Functions
- List and Tuple Object Functions
- Studying Dictionary Object Functions
- Defining Python Inbuilt Modules
- Studying Types of Modules
- os, sys, time, random, datetime, zip modules
- How to Create Python User Defined Modules?
- Understanding Pythonpath
- Creating Python Packages
- init File and Package Initialization
- What and How of File Handling with Python?
- How to Process Text Files using Python?
- Read/Write and Append File Object
- Test Operations: os.path
- Overview of Object Oriented Programming
- Defining Classes, Objects, and Initializers
- Attributes – Built-In Class
- Destroying Objects
- Methods – Instance, Class, Static, Private methods, and Inheritance
- Data Hiding
- Module Aliases and reloading modules
- Python Exceptions Handling
- Standard Exception Hierarchy
- .. except…else
- .. finally…clause
- Creating Self-Exception Class
- User-defined Exceptions
- Debugging Errors – Unit Tests
- Project Skeleton
- Creating and Using the Skeleton
- How to use pdb debugger?
- Using Pycharm Debugger
- Asserting Statement for Debugging
- Using UnitTest Framework for Testing
- Understanding Regular Expressions
- Match Function, Search Function, and the Comparision
- Compile and Match, Match and Search
- Search and Replace
- What and How of Extended Regular Expressions?
- Wildcard Characters
- Objectives of Functions
- Types of Functions
- Creating UDF Functions
- Function Parameters
- Unnamed and Named Parameters
- Creating and Calling Functions
- Python user Defined Functions
- Python packages Functions
- Anonymous Lambda Function
- Understanding String Object Functions
- List and Tuple Object Functions
- Studying Dictionary Object Functions
- Defining Python Inbuilt Modules
- Studying Types of Modules
- os, sys, time, random, datetime, zip modules
- How to Create Python User Defined Modules?
- Understanding Pythonpath
- Creating Python Packages
- init File and Package Initialization
- What and How of File Handling with Python?
- How to Process Text Files using Python?
- Read/Write and Append File Object
- Test Operations: os.path
- Overview of Object Oriented Programming
- Defining Classes, Objects, and Initializers
- Attributes – Built-In Class
- Destroying Objects
- Methods – Instance, Class, Static, Private methods, and Inheritance
- Data Hiding
- Module Aliases and reloading modules
- Python Exceptions Handling
- Standard Exception Hierarchy
- .. except…else
- .. finally…clause
- Creating Self-Exception Class
- User-defined Exceptions
- Debugging Errors – Unit Tests
- Project Skeleton
- Creating and Using the Skeleton
- How to use pdb debugger?
- Using Pycharm Debugger
- Asserting Statement for Debugging
- Using UnitTest Framework for Testing
- Understanding Regular Expressions
- Match Function, Search Function, and the Comparision
- Compile and Match, Match and Search
- Search and Replace
- What and How of Extended Regular Expressions?
- Wildcard Characters
- Data Visualization and Matplotlib, seaborn
- Python Libraries
- Features of Matplotlib
- Line Properties Plot with (x, y)
- Set Axis, Labels, and Legend Properties
- Alpha and Annotation
- Univariate plots
- Bivariate plots
- Multivariate plots
- Interpretations
• Data Manipulation and Machine Learning with Python
• Data Manipulation with Python – Pandas
• Understanding Pandas
• Defining Data Structures
• Data Operations(filtering, sorting, grouping, aggregation, merging) and Data Standardization
• Pandas: File Read and Write Support
• SQL Operations(pandasql)
1. Creating Database, using Database
2. Creating Tables, inserting Values in the Table
3. Select Query where clauses
4. And, Or, Not Operators
5. In Operators, Not In Operators
6. Between & Not Between Operators
7. Like Operators orders distinct Command
8. Distinct Clauses
9. Limit Clauses with where Clauses
1. Offset Arithmetic expression (SUM, AVG, MAX, MIN, COUNT) Update Functions
2. Inner, Left, Right, Outer, Fuller, Cross Join
3. Group By, Having clauses, Sub Query
4. Advanced Sub query using with Functions
5. Union And Union All
6. Advanced Union & Union all with Functions
7. Advanced Union & Union all using Where Clauses
8. Advanced Union & Union all using Joins
9. If And Case Statements
10. STRING FN & manipulation of STRINGS VALUE using String fn
11. Date Function
12. Date Format Functions
13. Delete And Truncate
14. Alter
15. Creating View Advanced concept
16. Procedure.
1. Power BI Introduction
2. Introduction to Power BI
3. Desktop, Getting Data (Excel and RDBMS, Web, SharePoint), Naming for Q&A,
4. Direct Query vs Import Data
5. Introduction to Modelling
6. Set Up and Manager
7. Relationships Cardinality and Cross Filtering
8. Creating Hierarchy in the Model
9. Introduction to Modelling
10. Default Summarization and Sort by Creating Calculated Columns
11. Creating Measures & Quick Measures
12. Creating Visuals Colour and Conditional Formatting
13. Setting Sort Order
14. Scatter and Bubble
15. Charts and Play Axis
16. Tool Tips, Slicers
17. Timeline Slicers and Sync Slicers
18. Cross Filtering and Highlighting
19. Creating Visuals Visual
20. Page & Report Level Filters
21. Drill Down/Up Hierarchies
22. Constant Lines Tables, Matrix and Table
23. Conditional Formatting KPI’s, Cards and Gauges
24. Map
25. Visualizations
26. Custom Visuals
27. Creating Visuals
28. Managing and Arranging
29. Drill Through
30. Custom Report Themes
31. Grouping and Binning
32. Bookmark & Buttons
33. DAX Expressions
34. Introduction to Modelling
35. Set Up and Manager
36. Relationships Cardinality and Cross Filtering
37. Creating Hierarchy in the Model
38. Introduction to Modelling
39. Default Summarization and Sort by
40. Creating Calculated Columns
41. Creating Measures & Quick Measures
42. Publishing and Sharing
43. Sharing Options
44. Publish from Power BI
45. Desktop Publish Reports to Web
46. Sharing Reports and Dashboards Workspaces
47. Apps. Sharing Options
48. Printing PDF’s and
49. Exports Row Level Security
50. Exporting Data from Visualization Refreshing
51. Datasets Understanding
52. Data Refresh Gateways
1. Excel and basic formula
2. Introduction to excel and its interface
3. Entering and editing data into cells & autofill
4. Basic Formating using shortcuts keys
5. Basic DAX – sum, Average,Count,
6. Sorting and filtering the data with accending & Decending Inserting
7. deleting and renaming ther worksheet
8. Ranges selection using range name for easier references
9. Conditional Formating
1. Logical Dax: If statements nested ifs, And & or operators
2. Lookup Functions: Vlookup Dax & Hlookup
3. AX for searching and retreiving data
4. Text DAX: Concatenation, LEFT, RIGHT,MID DATE & Time Function: Working with Date
using DAX
5. Introduction Pivot Creating PIVOT to summarise and analyse large dataset
6. Pivot table filtering and formating
7. Calculated fields in pivot table pivot charts Creating
8. Charts in excel
9. Chart formatting chart type and customization
10. Data analysis using chart Data
11. Validation:Setting data validation rules
12. Scenario Manager:Creating and managing scenarios
13. Text to columns: Splitting text into multiple columns using delimeters
14. Removing Duplicates Data
15. Consolidation Text
16. Functions for data using Trim , proper, upper, lower
17. What if Analysis:using tables for scenarios
18. Power Query: Importing transforming bigdata
19. Macros:Recording and running macros to automate task
20. Customizing Ribbon: Modifying the ribbon
21. Introduction to VBA
22. Enablind Developer tab
23. VBA Editor : Opening and Navigating
Programming Languages & Tools
Certificates
Obtaining Your Certification
Upon successful completion of any course at Etlhive, participants receive a certificate attesting to their proficiency in the respective subject matter. These certificates serve as tangible evidence of the skills acquired during the training, enhancing the credibility of individuals in the job market and validating their expertise to potential employers. Etlhive certificates are recognized for their industry relevance and are highly regarded by leading organizations, providing a competitive edge to certificate holders. The validation process ensures that the certifications are earned through rigorous learning and assessment methods, reflecting real-world application and mastery of the concepts taught. With Etlhive certificates, individuals can showcase their commitment to continuous learning and professional development, opening doors to new career opportunities and advancement prospects. Students can have option for IBM Certificate also.
Training students for leading brands
Frequently asked questions
Our Data Analytics course is open to anyone with a passion for working with data. Whether you are a fresher, a working professional, or someone looking to switch careers, you can benefit from this course. Basic knowledge of mathematics or statistics is helpful but not mandatory.
In this course, you will get hands-on experience with industry-standard tools such as Excel, SQL, Python, Power BI, and Tableau. These tools are crucial for data manipulation, analysis, and visualization.
Upon completing the course, you can explore career opportunities such as Data Analyst, Business Analyst, Data Consultant, or BI Analyst. Data Analysts are in high demand across various industries, including finance, healthcare, retail, and IT.
While some basic programming knowledge (like Python) is beneficial, our course is designed to teach you everything from scratch. We provide training in essential programming skills needed for Data Analytics, making it easy for beginners to grasp.
Various companies have certifications available for these kind of programs:
AWS Certified Machine Learning – Specialty certification
Professional Data Engineer Certification(Google)
Google Data Analytics Professional Certificate
Microsoft Certified: Azure Data Scientist Associate
Data science professionals(IBM)
and the list goes on.
Graduation in any stream, Freshers or working professionals who either wish to start their career as a Data Scientist or wish to switch from their previous profile into mainstream analytics.
Our Placements
We don’t give just assurances, we actually placed candidates
Testimonials
"I have completed an 8-month online data science course from the Etlhive-Wakad in Pune city. I took their structured online course to get me into the IT field, and I am very satisfied and proud of my decision. It is the best online course.
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