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Data Analytics Training With Guaranteed Placement Assistance
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Data Analytics Training Videos
Skills Covered in Data Analytics Training Course
Data analysis planning
Data analysis monitoring
Elicitation
Collaboration
Tick Managing requirements life cycle
Analyzing business strategies
Definition of business design
Analyzing business requirements
Details about business intelligence
Details about business architecture
Data Cleaning and Preparation
Data Visualization
Statistical Analysis
SQL for Data Analysis
Programming for Analytics
Data Analytics Training Key Features
You will have access to eleven case studies
You will have access to CBAP exam application assistance
You will have access to eleven quizzes that you can solve after the end of a lesson.
The course curriculum adheres to the strict standards set by BABOK® Guide Version 3.
Delivered by Industry Experts
Professional Resume building
Interview Preparation Session
Interpersonal Skills Training
Interactive Q&A Discussion
Designated Placement Advisor
Certification Guidance
Weekly Practice Assignments
Zero-Interest EMI Option
Classroom & Online Training
Weekdays & Weekend Classes
1 Year Access to Recorded Sessions
Assured Job Placement
Delivered by Industry Experts
Our Alumni Working in
Course Reviews
Arathi
Data Analyst
I have completed the Data Analyst course at Apponix Technologies. The trainers were excellent and very supportive, making it easier to grasp complex concepts. Thank you for providing such valuable guidance and support throughout the learning process.
Chitra
Data Analyst
I recently completed the Data Analyst course at Apponix Technologies. The trainers were knowledgeable and offered great support throughout the course. Their guidance made complex concepts easy to understand. I appreciate their efforts in helping us build strong analytical skills.
Sanjana
Data Analyst
I took the Data Analyst course at Apponix Technologies, and the experience was fantastic. The trainers were patient and provided thorough explanations, ensuring we understood each topic well. Their dedication truly enhanced my learning journey, making it a rewarding experience.
Our Recent Placements
Classroom Training
Classroom Training
Available Training Options
Online Training
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- Interactive Live Training Sessions
- Customized training based on client requirement
- Linkedin Learning Premium access for 12 months to all the participants
- Delivered by Industry Experts
Data Analytics Syllabus
Eligibility
To become a successful Data Analytics Professional, you can benefit from the guidance of our expert trainers. The best part about our training program is that all you need is a basic understanding of computers.
Pre-requisites
No prior knowledge or work experience is needed to take this Data Analytics course and learn all aspects of data analysis.
Throughout the course, you will gain all the necessary skills to become a proficient Data Analytics Professional, from mastering programming languages to using advanced analytical tools.
Data Analytics Training Course Syllabus
Data Analyst Project Life Cycle
Phase 1 - Data Collection
After carefully evaluating the business case in a particular domain, data will be collected surrounding it.
Phase 2 - Data Preparation
Using SQL, a database will be created to store the data collected in the previous step.
Phase 3 - Insights Generation And Dashboard Building
Establish a connection between the database and Tableau/Python/R tools to extract the required data. Generate user-friendly reports according to the business needs and develop the dashboard using Tableau/Power BI.
Projects
Analysis Of Patient Data (Domain: Healthcare)
This project requires learners to analyze the patient data of those suffering from different diseases across various summaries. The facility, chain organizations, and dialysis stations analysis is required to be carried out where the patients are undergoing dialysis. The project also focuses on the payment mode aspect wherein if any discounts or reduction in payments have happened then those are analyzed.
Loan Of Customers (Domain: Banking And Finance)
In this project, learners analyze the loan given by a financial institution to different customers of varied grades and sub-grade levels. The analysis needs to consider the loan disbursement reasons, funded amount, and revolving balance values for every customer in different states and geolocations. The project requires the customers payment modes and the last payment values.
Employee Retention (Domain: HR Analytics)
This HR-related project considers the attrition rate of employees working at an organization at different levels. The attrition rate analysis is done with respect to different factors such as monthly income, last promotion year, job role, and work-life balance of every employee of different departments
Industrial Combustion Energy Use (Domain: Energy)
The project requires learners to analyze the usage of different fuels in different facilities in different applications by finding the MMBTu and GWHt values. The fuels used for different geo-locations and for different primary titles are also taken into consideration while doing analysis.
Flights Delay Analysis (Domain: Aviation)
The primary aim of the project is to determine the different reasons behind the delay of flights of various airlines. The analysis needs to consider the number of flights in operation, the number of flights cancelled, and the statistical summary of week-wise, state-wise, and city-wise flight distributions.
Olist Store Analysis (Domain: ECommerce)
The market for a certain product is analyzed by considering a particular retail outlet which sells these products. The project involves statistical analysis on the payment distribution from different customers with the different modes of transactions across different product categories. The feedback from customers with respect to shipping days and other factors also needs to be considered while carrying out the analysis.
Excel
Introduction
- MS office Versions(similarities and differences)
- Interface(latest available version)
- Row and Columns
- Keyboard shortcuts for easy navigation
- Data Entry(Fill series)
- Find and Select
- Clear Options
- Ctrl+Enter
- Formatting options(Font,Alignment,Clipboard(copy, paste special))
Referencing, Named ranges,Uses,Arithemetic Functions
- Mathematical calculations with Cell referencing(Absolute,Relative,Mixed)
- Functions with Name Range
- Arithmetic functions (SUM,SUMIF,SUMIFS,COUNT,COUNTA,COUNTIFS,
- AVERAGE,AVERAGEIFS,MAX,MAXIFS,MIN,MINIFS)
Logical Functions
- Logical functions:IF,AND,OR,NESTED IFS,NOT,IFERROR
- Usage of Mathematical and Logical functions nested together
Referring data from different tables: Various types of Lookup, Nested IF
- LOOKUP
- VLOOKUP
- NESTED VLOOKUP
- HLOOKUP
- INDEX
- INDEX WITH MATCH FUNCTION
- INDIRECT
- OFFSET
Advanced Functions
- Combination of Arithmatic
- Logical
- Lookup functions
- Data Validation(with Dependent drop down)
Date and Text Functions
- Date Functions: DATE,DAY,MONTH,YEAR,YEARFRAC,DATEDIFF,EOMONTH
- Text Functions:
- TEXT,UPPER,LOWER,PROPER,LEFT,RIGHT,SEARCH,FIND,MID,TTC, Flash Fill
Data Handling::Data cleaning, Data type identification, Remove Duplicates, Formatting and Filtering
- Number Formatting(with shortcuts)
- CTRL+T(Converting into an Excel Table)
- Formatting Table
- Remove Duplicate
- SORT
- Advanced Sort
- FILTER
- Advanced Filter
Data Visualization: Conditional Formatting, Charts
- Conditional formatting (icon sets/Highlighted colour sets/Data bars/custom formatting)
- Charts: Bar,Column,Lines,Scatter,Combo,Gantt,Waterfall,pie
Data Summarization: Pivot Report and Charts
- Pivot Reports:Insert,Interface,CrosstableReports;Filter,Pivot Charts
- Slicers: Add,Connect to multiple reports and charts
- Calculated field, Calculated item
Data Summarization: Dashboard Creation, Tips and Tricks
- Dashboard:Types,Getting reports and charts together, Use of Slicers.
- Design and placement: Formatting of Tables,Charts,Sheets,Proper use of Colours and Shapes
Connecting to Data: Power Query, Pivot, Power Pivot within Excel
- Power Query: Interface, Tabs
- Connecting to data from other excel files, text files, other sources
- Data Cleaning
- Transforming
- Loading Data into Excel Query
Connecting to Data: Power Query, Pivot, Power Pivot within Excel
- Using Loaded queries
- Merge and Append
- Insert Power Pivot
- Similarities and Differences in Pivot and Power Pivot reporting
- Getting data from databases, workbooks, webpages
VBA and Macros
- View Tab
- Add Developer Tab
- Record Macro:Name,Storage
- Record Macro to Format table(Absolute Ref)
- Format table of any size(Relative ref)
- Play macro by button
- shape
- as command(in new tab)
- Editing Macros
- VBA:Introduction to the basics of working with VBA for Excel: Subs, Ranges, Sheets
- Comparing values and conditions
- if statements and select cases
- Repeat processes with For loops and Do While or Do Until Loops
- Communicate with the end-user with message boxes and take user input with input boxes, User Form
MySQL
Introduction to Mysql
- Introduction to Databases
- Introduction to RDBMS
- Explain RDBMS through normalization
- Different types of RDBMS
- Software Installation(MySQL Workbench)
SQL Commands and Data Types
- Types of SQL Commands (DDL,DML,DQL,DCL,TCL) and their applications
- Data Types in SQL (Numeric, Char, Datetime)
DQL & Operators
- SELECT
- LIMIT
- DISTINCT
- WHERE AND
- OR
- IN
- NOT IN
- BETWEEN
- EXIST
- ISNULL
- IS NOT NULL
- Wild Cards
- ORDER BY
Case When Then and Handling NULL Values
- Usage of Case When then to solve logical problems and handling NULL Values (IFNULL, COALESCE)
Group Operations & Aggregate Functions
- Group By
- Having Clause
- COUNT
- SUM
- AVG
- MIN
- MAX
- COUNT String Functions
- Date & Time Function
Constraints
- NOT NULL
- UNIQUE
- CHECK
- DEFAULT
- Primary key
- Foreign Key (Both at column level and table level)
Joins
- Inner
- Left
- Right
- Cross
- Self Joins
- Full outer join
DDL
- Create
- Drop
- Alter
- Rename
- Truncate
- Modify
- Comment
DML & TCL Commands
- DML
- Insert
- Update & Delete
- TCL
- Commit
- Rollback
- Savepoint
- Data Partitioning
Indexes and Views
- Indexes (Different Type of Indexes)
- Views in SQL
Stored Procedures
- Procedure with IN Parameter
- Procedure with OUT parameter
- Procedure with INOUT parameter
Function, Constructs
- User Define Function
- Window Functions
- Rank
- Dense Rank
- Lead
- Lag
- Row_number
Union, Intersect, Sub-query
- Union, Union all
- Intersect
- Sub Queries, Multiple Query
Exception Handling
- Handling Exceptions in a query
- CONTINUE Handler
- EXIT handler
Triggers
- Triggers - Before | After DML Statement
POWER BI
Power BI Introduction and Installation
- Understanding Power BI Background
- Installation of Power BI and check list for perfect installation
- Formatting and Setting prerequisits
- Understanding the difference between Power BI desktop & Power Query
The Power BI user interface, including types of data sources and visualizations
- Getting familiar with the interface BI Query & Desktop
- Understanding type of Visualisation
- Loading data from multiple sources
- Data type and the type of default chart on drag drop.
- Geo location Map integration
Sample dashboard with Animation Visual
- Finanical sample data in Power BI
- Preparing sample dashboard as get started
- Map visual Types and usages in different variation
- Understanding scatter Plot chart with Play axis and the parameters
Power BI artificial intelligence Visual
- Understanding the use of AI in power BI
- AI analysis in power bi using chart
- Q&A chat bot and the use in real life
- Hirarchy tree
Power BI Visualization
- Understanding Column Chart
- Understanding Line Chart
- Implementation of Conditional formating
- Implementation of Formating techniques
Power Query Editor
- Loading data from folder
- Understanding Power Query in detail
- Promote header, Split to limiter, Add columns, append, merge queries etc
Modelling with Power BI
- Loading multiple data from different format
- Understanding modelling (How to create relationship)
- Connection type, Data cardinality, Filter direction
- Making dashboard using new loaded data
Power Query Editor Filter Data
- Power Query Custom Column & Conditional Column
- Manage Parameter
- Introduction to Filter and types of filter
- Trend analysis, Future forecast
Customize the data in Power BI
- Understanding Tool tip with information
- Use and understanding of Drill Down
- Visual interaction and customisation of visual interaction
- Drill through function and usage
- Button triggers
- Bookmark and different use and implementation
- Navigation buttons
Dax Expressions
- Introduction to DAX
- Table Dax, Calculated column, DAX measure and difference
- Eg:- Calendar, Calendar auto, Summarize, Group by etc
- Calculated Column
- Related, Lookup value, switch, Datedif,Rankx,Date functions
- Dax Measure and Quick Measure
- Remove filters, Keep filters, All, Allselected, Time Intelligence Functions,Rollingaverage,YoY, Running total
Custom Visual
- Custom visual and understanding the use of custom
- Loading custom visual, Pinning visual
- Loading to template for future use
- Publishinhg Power Bi
Power BI Service
- Introduction to app.powerbi.com
- Schedule refresh
- Data flow and use power bi from online
- Download data as live in power point and more
Python
Anaconda Installation,Introduction to python,Datatypes,Opearators
- Variables,
- data types(integer,Boolean,Float,List,tuple,string),Opearators in python
Data types Contd,Slicing the data,Inbuilt functions in python
- Dictionaries,Sequencemethods,Concatenate,Repetition,len,min,maxfunctions,Indexposition,Addition and deletion of elements,Reverse,Sorting
Sets,SetTheory,RegularExpressions,Decision making statements
- Sets,re module(findall,search,split,match),if,elifGetting input from user,Identity Operators
Loops,Functions,Lambdafunctions,Modules
- For,Whileloops,Functions,Lambdafunctions,Mathmodule,Calendermodule,Date& time module
Pandas,Numpy,Matplotlib,Seaborn
- Data frame creation using different methods,Using Pandas anlysis on Universities,Salary data sets,Visualization using Matplotlib and Seaborn,Numpy introduction
ChatGPT
Introduction to ChatGPT and AI
- What is ChatGPT?
- The history of ChatGPT
- Applications of ChatGPT
- ChatGPT vs other chatbot platforms
- Industries using ChatGPT
- The benefits and limitations of ChatGPT
- Future developments in ChatGPT technology
- Ethical considerations related to ChatGPT and AI
Types of AI and Chatgpt architecture
- What is AI?
- Types of AI
- What is Machine Learning?
- Neural Networks
- Deep Learning
- Natural Language Processing (NLP)
- Computer Vision
- Robotics and AI
ChatGPT Functionalities and Applications
- How does ChatGPT work?
- ChatGPT Functionalities
- Drafting emails and professional communication
- Automating content creation
- Resume and Cover letter creation
- Research and information gathering
- Brainstorming ideas and creative problem solving
- Best Practices for Using ChatGPT
ChatGPT Prompt Engineering
- What is Prompt Engineering?
- Types of Prompts
- Crafting Effective Prompts
- Using ChatGPT to generate prompt
Call Us On
+91-80505-80888
Data Analytics Training Industry Project
Project 1
Sales Data Analysis
In this project, you will analyze a dataset containing sales records to identify trends, calculate key performance indicators (KPIs), and provide actionable insights. This involves using Excel and SQL to clean, manipulate, and visualize the data effectively.
Project 2
Customer Segmentation
You will work on a project that involves clustering customer data using Python and machine learning techniques. The goal is to identify distinct customer segments based on purchasing behavior, enabling targeted marketing strategies and personalized customer experiences.
Project 3
Interactive Data Dashboard
This project focuses on creating an interactive dashboard using Tableau or Power BI. You will integrate various data sources and design visualizations that allow stakeholders to explore data insights easily, enhancing decision-making processes within a business context.
Our Top Instructors
Mr. Sheikh
He is a working professional and has over a decade’s worth of experience in Data Analyst.He is currently associated with an MNC and is working as a Senior Platform Engineer.
Over the years, he has been our preferred Data Analyst trainer here at Apponix.
he has trained more than 500 students in Data Analyst.
He is a master when it comes to Data Analyst his students about configuration management tools.
Harsha
Education: I hold a degree from Dayananda Sagar, where I gained essential knowledge in data analytics and related technologies.
Professional Experience: With over 10 years of experience working in various MNCs, I am currently a Senior Data Analyst at Mindtree, focusing on extracting insights from data to drive business decisions.
Technical Expertise:
- Web Analytics: Analyzing web traffic and user behavior to enhance online presence.
- Social Media Analytics: Understanding social media trends and user engagement for better marketing strategies.
- Operational Analytics: Optimizing business operations through data-driven insights.
- Financial Analytics: Analyzing financial data to support strategic financial decisions.
- Risk Analytics: Identifying and mitigating potential risks through data assessment.
- Health Analytics: Leveraging data to improve health outcomes and operational efficiency.
- Data Analysis Planning: Strategizing data analysis initiatives for effective results.
- Data Analysis Monitoring: Continuously evaluating data processes to ensure quality and accuracy.
- Elicitation: Gathering requirements and insights to inform data projects.
- Collaboration: Working with stakeholders to achieve data-driven objectives.
- Managing Requirements Life Cycle: Overseeing the entire data requirement process from initiation to implementation.
Student Success & Career Support: I have successfully trained over 1000 students at Apponix Academy, maintaining an average rating of 4.8. My dedicated career support includes:
- Resume Building: Helping students create resumes that highlight their analytics skills and experiences.
- Interview Preparation: Providing guidance and practice for technical and behavioral interviews.
- Job Placement Assistance: Offering resources and strategies to help students secure positions in data analytics.
Maimuneesa Kazi
Education: I graduated from Karnataka University, where I developed a solid foundation in data analytics and business intelligence.
Professional Experience: With over 10 years of experience across various MNCs, I am currently a Senior Data Analyst at Accenture, specializing in transforming data into actionable insights.
Technical Expertise:
- Predictive Analytics: Utilizing statistical techniques to predict future outcomes based on historical data.
- Descriptive Analytics: Analyzing past data to understand trends and patterns.
- Prescriptive Analytics: Recommending actions based on data analysis to optimize decision-making.
- Data Mining: Extracting useful information from large datasets to uncover hidden patterns.
- Machine Learning: Implementing algorithms that allow systems to learn and improve from experience without explicit programming.
- Analyzing Business Requirements: Understanding and defining the needs of the business for effective data solutions.
- Business Intelligence: Leveraging data analytics for strategic decision-making and competitive advantage.
- Business Architecture: Structuring the business's operations to align with its strategy and goals.
Student Success & Career Support: I have trained over 1000 students at Apponix Academy, achieving an impressive average rating of 4.8. My comprehensive career support includes:
- Resume Building: Assisting students in crafting impactful resumes tailored for data analytics roles.
- Interview Preparation: Offering mock interviews and strategies to tackle common interview questions.
- Job Placement Assistance: Providing resources and guidance to help students secure job opportunities in data analytics.
Suhas
Education: I hold a degree from Global University, where I gained essential knowledge in data analytics and its applications.
Professional Experience: With over 9 years of experience in various MNCs, I am currently a Senior Data Analyst at Intel, specializing in leveraging data to drive business success.
Technical Expertise:
- Big Data Technologies: Managing and analyzing vast datasets for comprehensive insights.
- Database Management: Overseeing database systems to ensure optimal performance and accessibility.
- Business Intelligence (BI) Tools: Utilizing BI tools to convert data into actionable business insights.
- ETL Processes: Extracting, transforming, and loading data for analysis and reporting.
- Data Warehousing: Designing and implementing data warehousing solutions for efficient data storage and retrieval.
- Data Analysis Planning: Strategizing the approach to data analysis for impactful outcomes.
- Data Analysis Monitoring: Continuously tracking data analysis processes to ensure quality and effectiveness.
- Elicitation: Gathering and defining business requirements for data-driven solutions.
Student Success & Career Support: I have successfully trained over 1000 students at Apponix Academy, maintaining an impressive average rating of 4.8. My dedicated career support includes:
- Resume Building: Assisting students in creating resumes that highlight their analytical skills and experiences.
- Interview Preparation: Providing coaching and practice sessions for technical and behavioral interviews.
- Job Placement Assistance: Offering resources and strategies to help students secure positions in data analytics.
Moksha
Education: I graduated from Presidency University, where I laid the foundation for my expertise in data analytics.
Professional Experience: With over 15 years of experience in the field, I currently serve as a Senior Data Analyst at TCS, working with various MNCs to deliver data-driven solutions.
Technical Skills:
- Data Analysis Planning: Crafting effective strategies for analyzing data to drive insights.
- Data Analysis Monitoring: Overseeing analysis processes to ensure accuracy and relevance.
- Elicitation: Gathering requirements to understand stakeholder needs for data initiatives.
- Collaboration: Working closely with teams to enhance data analysis outcomes.
- Managing Requirements Life Cycle: Coordinating the progression of data requirements from inception to delivery.
- Analyzing Business Strategies: Evaluating business approaches to recommend data-driven improvements.
- Definition of Business Design: Structuring data projects to align with organizational goals.
- Analyzing Business Requirements: Translating business needs into actionable data solutions.
- Details about Business Intelligence: Applying BI techniques to convert data into strategic insights.
- Details about Business Architecture: Designing frameworks for effective data management and utilization.
- Data Collection and Management: Collecting and organizing data from diverse sources.
- Data Cleaning and Preparation: Ensuring data quality and readiness for analysis.
- Data Visualization: Creating visual representations of data to facilitate understanding.
- Statistical Analysis: Applying statistical methods to uncover patterns and trends.
- Predictive Analytics: Utilizing data to forecast future trends and behaviors.
- Descriptive Analytics: Summarizing historical data to understand past performance.
- Prescriptive Analytics: Offering recommendations based on data insights.
Student Success & Career Support: I have successfully trained over 1300 students at Apponix Academy, achieving an impressive average rating of 4.9. My dedicated career support includes:
- Resume Building: Assisting students in crafting compelling resumes that highlight their analytics expertise.
- Interview Preparation: Providing guidance and practice for both technical and behavioral interviews.
- Job Placement Assistance: Equipping students with the resources and strategies needed to secure roles in data analytics.
Overview of Data Analytics Training Course
The Data Analytics certification training with us is meticulously designed to offer a broad outlook of how one can be relevant in data analysis. Our practical trainers provide our learners with real-life instances, combined with case studies that can be incorporated into the field to produce tangible results. Real data challenges are under the focus of this training, so the participants get ready to work with actual difficulties.
By the end of this training, learners will understand what a Data Analyst does and how to present his work to people best. They will also learn techniques that will enable them to undertake analyses that will enable them to understand trends and facilitate insights and decision-making for business.
This comprehensive program covers all key areas of data analytics, including:
Data Collection and Cleaning: Find out how data is collected from various sources, cleaned and pre processed for use in data analysis.
Data Visualization: Avoid presenting a mass of facts and figures which numel, charts, graph, and dashboards can effectively present.
Statistical Analysis: Refresh your knowledge of statistical methods applied to the analysis of data to determine valuable patterns existent in a specific set of data.
Advanced Excel and SQL: Build experience in using Excel and SQL, essential for subsequent manipulation and querying of big data.
Benefits of learning data analytics
Taking a Data Analytics certification training offers numerous benefits, including:
- Comprehensive Skill Set: Gain sufficient basic knowledge on data warehousing, data mining, knowledge discovery process and methods for gaining useful insights from data.
- Proficiency in Essential Tools: Find out how to work with Excel, and SQL, and other useful tools and data visualization software, which are indispensable for Data Analytics.
- Critical Thinking and Analytical Skills: Build proficiency in data analysis and data interpretation skills that would help in the tracking of business trends and making of recommendations about business decisions.
- Enhanced Career Opportunities: This is where you increase your chances of finding a job by acquiring relevant skills for current employment market.
Related Job Roles for Data Analytics
Completing a Data Analytics certification opens doors to various career paths, including:
- Data Scientist: Convergence on data, forging, acute modelling, machine learning, and predictive analytics.
- Business Intelligence Analyst: Proven area of specialist focus–analyzing and applying data to business issues.
- Quantitative Analyst: In operation roles to work in organisations with finance to come up with models in investment.
- Operations Analyst: Use figures to optimize the productive mechanisms and make vital changes for the company’s functioning.
- Marketing Analyst: The information you get from the customers will help you in marketing and tracking the success of the campaigns.
Data Analyst Training Course
Who can take Data Analyst Training?
- Fresh graduates or non-graduates.
- Diploma holders with basic computer knowledge.
- BPO, Call center, or administrative professionals.
- Anyone interested in data analysis and data-driven decision-making.
Which course is best for Data Analysis?
- Data Analyst Bootcamp, Thinkful Data Analyst Bootcamp, IBM Data Analyst Professional Certificate, etc.
Data Analyst
Are the online classes and self-paced learning materials available throughout the year?
Yes, apart from accessing online and self-paced learning course materials, you will also have access to CBAP Exam Prep classes throughout the year!
Do I need experience in the relevant areas to be eligible for this course?
Yes, you would need to have at least 900 + hours in 4 – 6 relevant areas before you apply for this course.
