DATA ANALYTICS (SQL + Advanced XL + Python For Data Analyst + Power BI )
Data Analytics combo typically refers to the strategic combination of various data analytics tools, techniques, and technologies to derive valuable insights and make informed decisions from data. Data analytics involves the systematic analysis of data to uncover patterns, trends, and actionable insights.A Data Analytics combo involves using the appropriate combination of these methods and tools to extract valuable insights from data, whether it’s for business intelligence, marketing analysis, scientific research, or any other domain
Job Oriented Course for Data Analytics
Technologies Included in Data Analytics Course:-
- Oracle SQL
- Advanced XL
- Python For Data Analyst
- Power BI / Tableau
Duration of Training : 100 hrs
Batch type : Weekdays/Weekends
Mode of Training : Classroom/Online/Corporate Training
Data Analytics Training & Certification in Pune
Highly Experienced Certified Trainer with 10+ yrs Exp. in Industry
Realtime Projects, Scenarios & Assignments
Why Radical Technologies
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COURSE CONTENT :
ORACLE 12C SQL
SQL Basic
WEEK – 1 :
1] Introduction to Oracle DB 12c
Oracle Database 12c: Focus Areas, Fusion Middle-ware, Oracle Cloud, Services, Deployment Methods, RDBMS, Data Model, ER Diagram, Relation DB Terminology, SQL Statements and Development Environment, HR Schema Tables, DB Documentation.
2] Retrieving Data using SQL SELECT statement
Basic Select Statement, Arithmetic Expression, Defining Null values, Concatenation Operator & Literal, Duplicate Rows, Displaying table structure. Practice Overview.
3] Restricting & Sorting Data
Use of Where clause, Character Strings & Date Data, Comparison, Logical, Range, Pattern, & Other operators, Rules Precedence, Order By Clause, Substitution variables. Practice Overview
WEEK – 2 :
4] Single Row Function & Customized Output
SQL function, Single Row Function, Character/Case Functions, Number Functions, Nesting Function, Date Functions etc. Practice Overview.
5] Conversion & Conditional Expressions
Conversion Function, Implicit/Explicit Data Conversion, General Function with covering NVL, NVL2, NULLIF, COALESCE, Conditional Expression with Decode function and Case Expression. Practice Overview.
6] Reporting Aggregated Data using Group Function
Usages of Group function, Nesting Group Function, Creating Group Data, Restricting group data using Having Clause. Practice Overview.
7] Displaying Data from Multiple Tables using Join
Type of Joins, Explaining with Natural Join, Using Clause, On Clause, SQL 99 Syntax, Three-Way Join, Self Join, NonEqui Join, Inner Versus Outer Join, Cross Join etc. Practice overview.
WEEK – 3 :
8] Using Sub-queries to solve queries
Scenario to use Sub-query, Rules for Sub-queries, Type of Sub-queries, Single Row Sub-queries and Multi Row Sub-queries. Null value in Sub-query etc. Practice Overview.
SQL Advanced :
9] Using Set Operator
Set Operator Rules, Covering Set Operator as UNION, UNION ALL, INTERSECT, MINUS etc. Practice Overview.
10] Managing Tables using DML Statements
Data Manipulation Language, Covering INSERT, UPDATE, DELETE & DB Transaction control using COMMIT, ROLLBACK, SAVEPOINT. Use of For update clause in SELECT Statement.
11] Introduction to Data Definition Language
Database Object, Naming Rules, Various Data Types, CREATE TABLE statement, Constraint guidelines, Defining constraints as NOT NULL, UNIQUE, PRIMARY KEY, FOREIGN KEY & CHECK etc. ALTER TABLE, DROP TABLE Statement. Create Table using Sub-query. Practice Overview.
WEEK – 4 :
12] Introduction to Data Dictionary Views
Data Dictionary Structure, How to use Data Dictionary, USER_OBJECTS, ALL_OBJECT, USER_CONSTRAININTS. TABLE Information, Column Information etc. Practice overview.
13] Creating Sequence, Synonyms & Indexes
Importance of Sequence, Synonyms & Indexes. Defining Sequence, Synonyms & Indexes & DROP statement etc. Practice overview.
14] Creating Views
What’s View? Advantage of Views, CRETAE Simple & Complex views, Rules for performing DML operation on views. Modifying & Removing a view etc. Practice Overview.
15] Retrieving Data by using Sub-Query
Multiple Column Sub-query, Column Comparison, Pair & Non Pair Sub-queries, Scalar Sub queries, Co-related Sub-queries, Use of WITH clause. Practice Overview.
ADVANCED EXCEL
- Getting Started with Excel
- Understanding the Excel Interface
- Understanding the Menu System
- Understanding the Quick Access Toolbar
- Understanding Workbooks and Worksheets
- Understanding the Formula Bar
- Understanding the Status Bar
- Understanding the Navigation System of Excel
- Using shortcut menus and the Mini toolbar
- Creating new workbooks
- Using Save and Save as
- Data Generation Techniques
- Exploring Data Entry and Editing Techniques
- Entering Data with Autofill
- Understanding Undo and Redo
- Adding Comments
- Flash Fill Data
- Working on Formulas and Functions
- Creating Simple Formulas
- Copying Formulas to Adjacent Cells
- Working with Relative, Absolute and Mixed Referencing
- Using Sum and Average
- Using Text Functions
- Using Date Functions
- Using Lookup Functions
- Using Conditional Functions
- Using Logical Functions
- Data Formatting and Presentation
- Basic Cell Formatting
- Data and Table Formatting
- Exploring Font Styles and Effects
- Adjusting Row Heights and Columns
- Working with alignment and Wrap Text
- Working with Borders
- Using conditional formatting
- Creating and using tables
- Data Filtering and Management
- Filtering Data using Data Filter
- Advanced Filtering Techniques
- Organizing Data using Data Sort
- Custom Sorting Techniques
- Working With Pivot Tables
- Creating a Branch wise and opened by Pivot
- Creating a Date Wise Pivot
- Creating a Count of Account Types Pivot
- Creating a Graph out of Pivot Data
Introduction to Python programming
Variables, data types, operators, and expressions
Control structures (if/else, loops)
Functions, modules, and packages
Data structures in Python
Lists, tuples, dictionaries, and sets
Indexing, slicing, and manipulating data structures
Python for data analysis
Reading and writing data from various formats (CSV, Excel, JSON, SQL)
Exploratory data analysis with Pandas library
Cleaning, transforming, and aggregating data
Predictive modeling and machine learning algorithms
Visualizing data with Matplotlib and Seaborn
evaluation and selection
Working with time series data
Case studies and projects
Analyzing real-world datasets and solving data-related problems
Developing data-driven insights and recommendations.
Data Transformations :
- Introduction to Power BI Desktop
- Changing Locale
- Connecting to a Database
- Basic Transformations
- Managing Query Groups
- Splitting Columns, Changing Data Types, Working with Dates
- Removing and Reordering Columns
- Conditional Columns
- Merge Queries
- Query Dependency View
- Transforming Less Structured Data
- Query Parameters
Data Modelling :
- Managing Data Relationships
- Creating Calculated Columns
- Optimizing Models for Reporting
- Creating Calculated Measures
- Creating and Managing Hierarchies
- Using Calculated Tables
- Time Intelligence
- Include and Exclude features
- Grouping and Binning
Visualizing your Data :
- Introduction to charts: Pie, Tree map, Combo charts, Map Visualizations, Scatter plot, Table, Matrix, Gauge, Card, Shapes, Textboxes, Images and KPI
- Filter (Including TopN), Date Slicer
- Coloring Charts
- Page Layout, Positioning, Aligning, Sorting Visuals and Formatting
- Visual Relationship
- Custom Hierarchies
Working with PBI Service :
- Overview of Dashboards and Service
- Uploading to Power BI Service
- Configuring a Dashboard
- Dashboard Settings
- In-Focus Mode
- Pinning a Live Page
- Custom URL and Title
- Export to CSV and Excel
- Power BI Notifications
- Publishing to Web
Working with Excel :
- Importing Excel Data using Simple Table
- Connecting to Excel Workbook on OneDrive for Business
- Pinning Excel Tables or Visuals
Organization Packs, Security and Groups :
- Creating a Group
- Creating, Using and Editing a Content Pack
- Row Level Security
- Data Classification
- Creating and Using Custom Visuals
Student Stories and Reviews :
FAQs :
This course covers a comprehensive range of topics including data analysis techniques, Oracle SQL fundamentals, and Python programming for data analysis.
Upon successful completion of the training, participants receive certification validating their proficiency in Advanced Excel and Power BI tools.
The curriculum includes modules on SQL database querying, Python programming for data manipulation and analysis, and Tableau for data visualization and dashboard creation.
There are no strict prerequisites, but having basic knowledge of Excel and familiarity with data analysis concepts would be beneficial.
This training equips participants with essential skills in both Python programming and Advanced Excel, enhancing their capabilities as data analysts and increasing their employability.
Yes, the training caters to beginners as well as intermediate learners, providing a solid foundation in Power BI, Tableau, and Oracle SQL.
Participants work on real-world projects where they apply Python and Tableau skills to analyze data, create visualizations, and derive insights.
Yes, several institutes offer certification programs upon completion of Caesar II Training, validating the participant’s proficiency in pipe stress analysis using the software.
Participants learn how to integrate SQL querying, Python data manipulation, and Excel analysis techniques to solve complex data problems commonly encountered in the industry.
This training combines essential skills in Oracle SQL querying and Power BI data visualization, offering a well-rounded foundation in data analytics.
Graduates can pursue roles such as Data Analyst, Business Analyst, Data Scientist, or Visualization Specialist in various industries.
The course employs a mix of theoretical concepts, hands-on exercises, and practical projects to accommodate diverse learning preferences.
The training is typically instructor-led, providing participants with personalized guidance and support throughout the learning journey.
Participants learn data manipulation with Excel, programming with Python, and visualization with Tableau, equipping them for diverse data analytics roles.
The curriculum is regularly updated to reflect current industry trends and incorporates best practices in data analysis, ensuring participants are well-prepared for the job market.
This training not only covers Oracle SQL querying techniques but also delves into advanced data visualization and dashboard creation using Power BI.
Yes, the program is designed to cater to individuals from diverse backgrounds, offering comprehensive training in Python, Tableau, and data analytics concepts.
Participants work on projects involving data cleansing, analysis, visualization, and reporting using Excel, Tableau, and Oracle SQL.
The course content is tailored to match current industry requirements, ensuring participants acquire skills that are in high demand in the job market.
The duration varies depending on the course format, but typically ranges from several weeks to a few months.
Participants engage in hands-on exercises where they analyze datasets, create visualizations, and build dashboards using Tableau and Excel tools.
Participants learn how to leverage Python for data manipulation, Power BI for visualization, and Oracle SQL for querying, enabling them to perform comprehensive data analysis tasks.
This course not only covers Advanced Excel and Python programming but also provides certification in Tableau, adding value to participants’ resumes.
Graduates can explore career opportunities in fields such as data analysis, business intelligence, database management, or data visualization.
Yes, the training program is designed to accommodate individuals from diverse backgrounds, including those transitioning to data analytics careers.
Participants work on projects involving data cleaning, transformation, analysis, and visualization using Advanced Excel and Power BI tools.
Participants gain hands-on experience in SQL querying and Python programming, enabling them to tackle real-world data analysis tasks effectively.
This course offers a comprehensive blend of Tableau, Excel, and Python training, providing participants with a versatile skill set
Most Probable Interview Questions for Data Analytics
Interview Question No. 1 for Data Analytics : Can you explain your experience with Oracle SQL and how you’ve utilized it in previous data analytics projects?
Interview Question No. 2 for Data Analytics : How comfortable are you with performing advanced data analysis tasks in Excel, and can you provide examples of your proficiency?
Interview Question No. 3 for Data Analytics : Describe a scenario where you used Python for data manipulation and analysis. What challenges did you face, and how did you overcome them?
Interview Question No. 4 for Data Analytics : In what ways have you leveraged Power BI for data visualization and dashboard creation in your previous projects?
Interview Question No. 5 for Data Analytics : Can you discuss your experience with Tableau and how you’ve utilized it to communicate insights derived from data analysis?
Interview Question No. 6 for Data Analytics : How do you approach integrating Oracle SQL, Python, and Excel in your data analysis workflow to derive meaningful insights?
Interview Question No. 7 for Data Analytics : Describe a complex data analysis project you worked on that involved the use of multiple tools such as Oracle SQL, Python, and Power BI/Tableau.
Interview Question No. 8 for Data Analytics : How do you stay updated with the latest trends and best practices in data analytics, particularly in relation to Oracle SQL, Python, Excel, Power BI, and Tableau?
Interview Question No. 9 for Data Analytics : Can you walk us through your process of transforming raw data into actionable insights using Oracle SQL, Python, and Excel?
Interview Question No. 10 for Data Analytics : Discuss a situation where you encountered performance issues while querying data with Oracle SQL and how you resolved them.
Interview Question No. 11 for Data Analytics : What are some common challenges you’ve faced when working with large datasets in Excel, and how have you addressed them?
Interview Question No. 12 for Data Analytics : Can you provide examples of how you’ve utilized Python libraries such as Pandas and NumPy for data manipulation and analysis?
Interview Question No. 13 for Data Analytics : Describe a time when you had to create interactive and insightful visualizations using Power BI or Tableau to present complex data findings.
Interview Question No. 14 for Data Analytics : How do you ensure the accuracy and reliability of your data analysis results when working with multiple tools like Oracle SQL, Python, and Excel?
Interview Question No. 15 for Data Analytics : Can you discuss a project where you had to extract, transform, and load (ETL) data using Oracle SQL and Python scripts?
Interview Question No. 16 for Data Analytics : What strategies do you employ to optimize the performance of Power BI reports and dashboards, especially when dealing with large datasets?
Interview Question No. 17 for Data Analytics : How do you approach data storytelling when presenting your findings using Tableau or Power BI, and what techniques do you find most effective?
Interview Question No. 18 for Data Analytics : Can you share your experience with automating repetitive tasks in Excel using VBA (Visual Basic for Applications)?
Interview Question No. 19 for Data Analytics : Discuss a scenario where you had to collaborate with team members who had varying levels of expertise in data analytics tools like Oracle SQL, Python, and Excel.
Interview Question No. 20 for Data Analytics : How do you prioritize tasks and manage deadlines when working on multiple data analytics projects simultaneously, especially those involving different tools and technologies?
Learn Data Analytics (SQL + Advanced XL + Python For Data Analyst + Power BI) Combo – Course in Pune with Training, Certification, & Guaranteed Job Placement Assistance!
At Radical Technologies, we are passionate about empowering individuals with the skills and knowledge they need to thrive in the rapidly evolving field of data analytics. Located in Pune, our institute stands as a beacon of excellence, offering comprehensive training programs tailored to meet the demands of the modern workforce.
With a focus on providing practical, hands-on learning experiences, we equip our students with the expertise needed to excel in today’s data-driven world. Our flagship Data Analytics Combo Course encompasses a diverse range of essential technologies, including Oracle SQL, Advanced Excel, Python, Power BI, and Tableau.
Led by industry experts and experienced instructors, our courses delve deep into each technology, ensuring a thorough understanding of its applications and functionalities. Whether you’re a seasoned professional looking to upskill or a fresh graduate eager to enter the world of data analytics, our programs cater to learners at all levels.
Our commitment to excellence extends beyond classroom instruction. We provide comprehensive training and certification, validating our students’ proficiency in each technology. These certifications serve as a testament to their expertise, enhancing their credibility in the job market and opening doors to exciting career opportunities.
At Radical Technologies, we believe in fostering a collaborative learning environment where students can engage with peers, share ideas, and tackle real-world challenges together. Our state-of-the-art facilities and modern teaching methodologies ensure an immersive and enriching learning experience for all.
Join us at Radical Technologies and embark on a transformative journey towards becoming a skilled data analyst. Whether you aspire to unlock insights from complex datasets, build interactive dashboards, or drive data-driven decision-making processes, we provide the tools and guidance you need to succeed. Take the first step towards a rewarding career in data analytics with Radical Technologies, where knowledge meets innovation, and possibilities are limitless.
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Data Analytics Combo Online Training from India | Excel | Autofill | Demo - Radical Technologies
