Data Analytics Program
November 17, 2024 2024-12-06 16:22Data Analytics Program
Data Analytics Program
Combines data analytics with essential tools and techniques.
Provides hands-on experience through real-world projects.
Taught by industry professionals with deep expertise in analytics.
Covers key tools like Python, SQL, Tableau, and Excel.
Opens doors to diverse roles in data analysis, business intelligence, and analytics.
Keeps you updated with the latest technologies and industry trends.
Offers skills that are applicable across various industries and roles.
Provides opportunities to network with peers and experts in the data field.
Key Highlights of Data Analytics Program
Weekly sessions with industry professionals
Dedicated Learning Management System
300 hours of hands-on learning experience
Over 90 hours live sessions spread across 04 months
90 hours of self-paced Learning
Learn from Industry experts
More than 40+ industry-related projects and case studies
Personalized mentorship sessions with industry experts
24/7 Support
Dedicated Learning Management Team
One-one mentoring
No-Cost EMI Option
Designed for both working professionals and fresh graduates
High Demand and Career Opportunities
Competitive Edge and Innovation
Problem-Solving and Critical Thinking
Weekly sessions with industry professionals
Dedicated Learning Management System
300 hours of hands-on learning experience
Over 90 hours live sessions spread across 04 months
90 hours of self-paced Learning
Learn from Industry experts
More than 40+ industry-related projects and case studies
Personalized mentorship sessions with industry experts
24/7 Support
Dedicated Learning Management Team
One-one mentoring
No-Cost EMI Option
Designed for both working professionals and fresh graduates
High Demand and Career Opportunities
Competitive Edge and Innovation
Problem-Solving and Critical Thinking
Why Join Data Analytics Program?
Upcoming Batch on 7th Dec 2024
Data Analytics Program Overview
This program helps participants become proficient in data analysis. It includes Advanced Excel, Python, Statistics, SQL, and data visualization using Tableau. By mastering these skills, participants will gain a strong foundation in data management, analytics, and visualization techniques, empowering them to turn raw data into actionable insights.
Data Analytics Program Objectives
The program is aimed at offering essential knowledge in key areas related to data analysis, Python programming, and statistical techniques useful for data-driven decision-making. It emphasizes SQL for database management and Tableau for data visualization. The course is designed to make participants well-versed in data analytics, equipping them with the skills needed to analyze data and generate actionable insights.
Why Learn Data Analytics Program?
Data Analytics Program Advantages
Master essential tools like Advanced Excel, Statistics, Python, SQL & Tableau .
Learn up-to-date practices that match current market demands in data analysis.
Apply knowledge through real-world projects and hands-on exercises for practical experience.
Equip yourself with versatile skills applicable across various industries and sectors.
Develop data visualization expertise to effectively communicate insights and drive decision-making.
Stay ahead with in-demand tools that shape the future of data analytics.
Master essential tools like Advanced Excel, Statistics, Python, SQL & Tableau .
Learn up-to-date practices that match current market demands in data analysis.
Apply knowledge through real-world projects and hands-on exercises for practical experience.
Equip yourself with versatile skills applicable across various industries and sectors.
Develop data visualization expertise to effectively communicate insights and drive decision-making.
Stay ahead with in-demand tools that shape the future of data analytics.
Data Analytics Program Certification
Data Analytics Learning Path/Curriculum
Module 01 - Advanced Excel
Lecture 01: Orientation (Introduction to Data Science, Scope of Data Science)
Lecture 02: Microsoft Excel Overview, Basic Navigation and Usage, Cell Referencing, Formatting Excel, Advanced Formatting, Shortcuts and Basic Formulas
Lecture 03: Sorting, Filtering, Advanced Filtering, Charts, Types of Charts, Advanced Charting Techniques, and Pivot Tables (Creating, Grouping and Summarizing Data)
Lecture 04: Lookup Functions, VLOOKUP, Using VLOOKUP with Multiple Criteria, HLOOKUP, Combining HLOOKUP with Other Functions, MATCH Function, Using MATCH for Dynamic Referencing
Lecture 05: Introduction to VBA & Macros, Understanding VBA Basics, Debugging and Error Handling, Advanced VBA Techniques, Integrating VBA with Excel Functions, Designing Effective Dashboards, Building a Dashboard
Lecture 06: Understanding the Basics of Data Analysis, Data Import and Cleaning, Using Formulas and Functions, Data Visualization, Descriptive Statistics
Lecture 07: Advanced Data Analysis Techniques, DAX, Scenario and Sensitivity Analysis, Dashboards and Reports, Case Studies and Real-World Applications, Practical Examples of Data Analysis in Excel
Module 02 - Python
Lecture 08: Introduction to Python, Why Python, Variables, Data Types, Type Casting, Strings, Indexing
Lecture 09: Operators and Conditional Statements, Looping Statements and its Control Statement
Lecture 10: Lambda Functions, *args, **kwargs, Functions
Lecture 11: Data Structures - List, Tuple, and List Comprehensions
Lecture 12: Data Structures - Set and Dictionaries
Lecture 13: Classes, Objects and Constructors, Inheritance
Lecture 14: Polymorphism, Abstraction and Encapsulation
Lecture 15: Connecting to Databases, Establishing Connections to Databases, Executing SQL Queries, ORM (Object-Relational Mapping), Working with NoSQL Databases
Lecture 16: Introduction to NumPy and Pandas
Lecture 17: Introduction to Seaborn and Matplotlib
Module 03 - Statistics
Lecture 18: Introduction to Statistics, Descriptive Statistics, Sample, Population, Measures of Central Tendency, Standard Deviation
Lecture 19: Variance, Range, IQR, Outliers, Correlation, Covariance, Skewness, Kurtosis, Probability
Lecture 20: Probability, Probability Distributions, Central Limit Theorem, Binomial and Poisson Distribution
Lecture 21: Normal Distribution, Type I & Type II Error
Lecture 22: T-test, Z-test, Hypothesis Testing Interview Questions
Module 04 - SQL
Lecture 24: Basics of Database, Types of Database, Data Types, SQL Operators, Expressions, Create, Insert
Lecture 25: Drop, Truncate, Delete, Alter, Update, Select, Range, Operator, IN, Wildcard, Like, Clause
Lecture 26: Constraint, Aggregation Function, Group By, Order By, Having
Lecture 27: Joins, Case, Complex Queries, Doubt Clearing
Module 05 - Tableau
Lecture 28: Tableau Desktop, Tableau Products
Lecture 29: Data Import, Measures, Filters
Lecture 30: Data Transformation, Marks, Dual Axis
Lecture 31: Manage Worksheets, Data Visualization, Dashboarding, Project
Lecture 32: Project Session
Lecture 33: Orientation Session (Introduction to Gen AI)
Data Analytics Program Skills Covered
Advanced Data Analysis
Python Programming
Data Visualization
Statistical Analysis
SQL Expertise
Dashboard creation
Data Storytelling
Data Analytics Program Tools Covered
Data Analytics Program Benefits
Projects that you will Work On
Machine Learning
No. of Projects:10
Deep Learning
No. of Projects:10
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Job Obligation after this course
We can apply for jobs inÂ
Build in-depth reports and dashboards using Tableau and Excel to efficiently communicate data insights to stakeholders.
Collect, clean, and analyze data to extract meaningful insights that inform strategic business decisions.
Develop and optimize SQL queries to maintain performance and accuracy in relational databases.
Apply statistical and analytical methods to identify trends, patterns, or opportunities within datasets.
Provide data-driven insights to support business objectives and decision-making.
Automate routine data processes using Python, enhancing efficiency and reducing manual workloads.
Collaborate with IT, management, and cross-functional teams to implement data-driven solutions.
Stay updated on trends and advancements in data analytics to maintain expertise and relevance.
Companies Hiring for this course
Admission Details
The application process consists of three simple steps. An offer of admission will be made to selected candidates based on the feedback from the interview panel. The selected candidates will be notified over email and phone, and they can block their seats through the payment of the admission fee.
Course Fees & Financing
Course Fees
(Inclusive Of All Taxes)
Payment Partners
We partnered with financing companies to provide competitive finance option at 0% interest rate with no hidden costs
Upcoming Batches/Program Cohorts
| Batch | Date | Time | Batch Type |
|---|---|---|---|
| Online Live Instructor Led Session | 7th December 2024 | 10:00 AM | Full-Time |
| Online Live Instructor Led Session | 20th December 2024 | 02:00 PM | Part-Time |
Comparison with Others
| Feature | Our Program | Other Programs |
|---|---|---|
| Comprehensive Coverage | Covers both foundational and advanced data analytics tools and techniques. | Often focuses on basic data analytics concepts without advanced techniques. |
| Tools Covered | Advanced Excel, Python, SQL, Tableau, and Power BI. | Primarily includes Excel, SQL, and Tableau, with limited coverage of additional tools. |
| Real-World Application | Hands-on projects that solve real-world problems through data analytics. | May include projects, but typically limited to theoretical exercises. |
| Expert-Led Instruction | Courses taught by industry professionals with expertise in data analytics. | Often taught by instructors with basic experience in data analytics. |
| Career Opportunities | Prepares students for roles like Data Analyst, Business Analyst, Business Intelligence Analyst. | Generally prepares students for entry-level roles like Data Analyst. |
| Future-Proof Skills | Focuses on emerging trends and advanced techniques in data analytics to ensure skills remain relevant. | Primarily focused on traditional methods, which may lack future-readiness. |
| Cross-Industry Relevance | Skills applicable across multiple industries, including tech, finance, healthcare, and more. | Skills are often tailored to specific industries, limiting versatility. |
| Networking Opportunities | Provides networking with peers and professionals in the data analytics field. | Networking is typically limited to local or small-scale industry groups. |
| Investment Value | Offers a strong return on investment by covering a broad range of advanced data analytics skills. | Provides value but lacks the advanced techniques that can significantly boost career potential. |
Data Analytics Program Training Faqs
Data analytics approaches are combined in a package called Data Analytics. It addresses the gathering, processing, statistical analysis, deep learning, and machine learning of data. Students work on real-world projects while learning how to utilize technologies like TensorFlow, R, Python, and PyTorch. The curriculum focuses on producing fresh data and insights from massive datasets, preparing graduates for positions as machine learning specialists, AI engineers, and data analysts.
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Join this course and to learn Combine data analytics with AI for insightful business solutions.
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Data analytics: Methods for gathering, processing, and analyzing data are called data analytics.
Statistical Analysis: Advanced techniques for deciphering data trends are known as statistical analysis.
Machine Learning: Model building for predictions.
Generative AI: GANs and transformer models are studied in generative AI in order to produce fresh data.
Deep Learning: Analytics and generative tasks using neural networks.
Programming Language: TensorFlow, PyTorch, R, and Python are programming languages.
Data visualization: is the process of giving data meaningful visual representations.
Real-World Projects:Â Useful, practical undertakings.
Data analysis techniques, Advanced Excel features, data cleaning, analysis techniques, Python programming, data visualization, statistical analysis, Descriptive statistics, probability theory, Database management, SQL querying, Dashboard creation, data storytelling, Generative AI concepts, AI model training
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Advanced Excel, Python, NumPy, pandas, matplotlib, SQL, Tableau/Power BI, Transformers, Hugging Face libraries, DALL-E model, OpenAI, PyTorch
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A degree in Engineering, Mathematics, Science, or any equivalent field is ideal for pursuing a career in Artificial Intelligence and Data Science.
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INR Fees(Exclusive of GST) : INR 144,999.00 INR Fee(Inclusive of GST) : INR 171,099.00 USD Fee: 1735(subject to be change).
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After completion of this course you have opportunity on the posts:Data Analyst, AI Engineer, Machine Learning Specialist
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Entry-level: 8-15 lakhs per annum
Mid-level: 15-30 lakhs per annum
Senior-level: 30+ lakhs per annum
40+ Projects done
1, we will optimise linked in profile and the algorithm of linkedin profile
2, We will conduct GITHUB and Kaggle sessions
3,We will do multiple Hackathons and guide you in problem solving skills for the interview process
4, We will ensure peer learning session are being conducted
5, We will issue mini certification for every tools.
6,We will asign you a personal mentor on pre booking i’t a one one session.
