Do I need to have a programming background to enter the Data Analytics field?

No, you don’t necessarily need a programming background to enter the Data Analytics field. However, having some knowledge of programming languages like Python or R can be beneficial in handling and analyzing large datasets. It’s important to note that many data analytics tools and platforms provide user-friendly interfaces that require minimal coding. So, while programming can enhance your data analytics skills, it’s not a strict prerequisite.

How does Data Analytics skill boost my career?

Data Analytics skills can significantly boost your career as these skills are in high demand across various industries. These skills can help you make informed decisions, predict trends, and improve business strategies. This in turn can make you a valuable asset to any organization, opening up opportunities for promotions and higher compensation. Additionally, the demand for data analytics skills spans across various industries, providing a wide range of career options.

What is Data Analytics?

Data Analytics is the process of examining, cleaning, transforming, and modeling data with the goal of discovering useful information, drawing conclusions, and supporting decision-making. It involves a range of techniques and methodologies to analyze data from various sources in different formats, both structured and unstructured. Data Analytics allows us to make sense of vast amounts of data to help businesses and organizations make better decisions and predictions.

What are the differences between Data Science and Data Analytics?

Data Science and Data Analytics are two distinct fields, though they overlap in many areas. Data Science is a broader field that includes various techniques such as machine learning, predictive modeling, and statistics. It involves extracting insights from various forms of data, both structured and unstructured. Data Science is more focused on designing and constructing new processes for data modeling and production using prototypes, algorithms, predictive models, and custom analysis. On the other hand, Data Analytics is more focused on processing and performing statistical analysis on existing datasets. It is used to discover useful information and aid in business decision making. Data Analytics tends to be more business and process-oriented. In summary, while both fields involve working with data, the main difference lies in the scope and focus of what they do with this data.

What are the salaries of a Data Analytics professional in India and US?

The salaries of a Data Analytics professional can vary widely depending on the industry, location, and level of expertise. In India, the average salary for a Data Analyst is around ₹6,72,500 per year. If a person has experience of 2-3 years then the figure may increase up to ₹6-8 LPA. However, the salary can go up to as much as INR 22 LPA. In the United States, the average salary for a Data Analyst is around $67,063 per year. However, the salary can range from $49,000 to $92,000 per year, and can even go up to $120,591 per year depending on the level of expertise and the state they are working in. Please note that these figures are averages and actual salaries can vary. It’s also important to note that salaries are often accompanied by other forms of compensation such as bonuses, profit sharing, and benefits. SOURCE: Payscale.com and Indeed.com

What is the career path of a Data Analytics professional?

The career path of a Data Analytics professional can vary based on individual goals and interests, but a typical path might look like this: Entry-Level Positions: After obtaining a relevant degree, one might start as a Data Analyst or Junior Data Analyst, where the primary tasks involve processing and making sense of large datasets. Mid-Level Positions: With a few years of experience, one could move into roles such as Senior Data Analyst, Data Scientist, or Data Engineer, where the tasks might involve more complex data analysis, predictive modeling, and machine learning. Management and Beyond: After gaining substantial experience and expertise, one could aim for leadership roles such as Data Analytics Manager, Director of Data Science, or even Chief Data Officer. These roles involve overseeing data strategies and policies in an organization.

Data Analytics & AI Course: Future Proof Your Analyst Career

Batch starts	Batch Timing	Early bird discount 10%	Offer Code Expires in
02 August 2025 02 August 2025 Sat - Sunday 07:30 AM - 10:30 AM (IST)	Sat - Sunday 07:30 AM - 10:30 AM (IST)	10% off (Valid Till 12th Jul) 10% off (Valid Till 12th Jul) Use code DTAN2313	12 July 2025 23:59:59	10% off (Valid Till 12th Jul) 10% off (Valid Till 12th Jul)	Use code DTAN2313	12 July 2025 23:59:59
11 October 2025 11 October 2025 Sat - Sunday 07:30 AM - 10:30 AM (IST)	Sat - Sunday 07:30 AM - 10:30 AM (IST)	10% off (Valid Till 21st Sept) 10% off (Valid Till 21st Sept) Use code DTAN2314	21 September 2025 23:59:59	10% off (Valid Till 21st Sept) 10% off (Valid Till 21st Sept)	Use code DTAN2314	21 September 2025 23:59:59
20 December 2025 20 December 2025 Sat - Sunday 07:30 AM - 10:30 AM (IST)	Sat - Sunday 07:30 AM - 10:30 AM (IST)	10% off (Valid Till 30th Oct) 10% off (Valid Till 30th Oct) Use code DTAN2323	30 October 2025 23:59:59	10% off (Valid Till 30th Oct) 10% off (Valid Till 30th Oct)	Use code DTAN2323	30 October 2025 23:59:59

	Is business analytics or Data Analytics certificate worth it?
	A certification in Data Analytics field demonstrates your ability and the commitment for the field. It also shows the employers that you have the ability to analyse data through complex data analytics methods, help the customer make strategic decisions, and develop sustainable business growth
	Which certification is the best for business analytics?
	There are several business analytics certifications. Some of the top ones are as follows: IIBA Certification in Business Data Analytics (CBDA) – Our course includes this certification preparation. Coursera Data Analytics Certification Google Data Analytics Certification IBM Data Analytics Certification
	Should I do business analytics course or an MBA?
	There are couple of ways you can master Data Analytics skills – Long term masters program (12 months to 18 months) or short term certificate course like this one. It depends on your current background. Long term programs are better suited for freshers (any graduate) or professionals with 1-2 years’ experience in programming or tech roles. Short term certification courses are designed for experienced professionals, specially with non-programming background. If you are a Business Analyst, Project Lead or a Manager or a testing professional, you can go for the short-term courses.
	How long does it take to get a data analytics certification course?
	Our Data Analytics online training course is 6 weeks long. All the short term courses vary from 6 weeks to 9 weeks. Though MBA or PG program in Analytics are of 12-18 months duration.
	Is data analyst easier than coding?
	Data Analysis is a very broad term. One can make a career in Data Analysis as a tech person with programming skills or without programming skills. For example, a reporting analysis and data visualization professionals don’t need much of programming skills. But roles like Data Engineers and Data scientists are completely technical role and need good programming skills.
	What course should I do for data analyst?
	We have provided an answer to this question in this FAQ section - Should I do business analytics course or an MBA?
	What is the best way to learn data analytics?
	Data Analytics is a vast field, so start by assessing your aptitude. Whether you're from a programming or non-programming background, a Data Analytics Certification Course helps you choose the right path. Would you like to talk to our experts. Drop us an email at info@techcanvass.com with your background.
	Which company hires most data scientist in India?
	Generally, most of the top IT companies are doing projects in Data Analytics. Some of the top employers are: Accenture Analytics. Mu Sigma. Swiggy. Amazon. Tiger Analytics. Oracle. Latent View. Microsoft.
	Do I need to have a programming background to enter the Data Analytics field?
	No, you don’t necessarily need a programming background to enter the Data Analytics field. However, having some knowledge of programming languages like Python or R can be beneficial in handling and analyzing large datasets. It’s important to note that many data analytics tools and platforms provide user-friendly interfaces that require minimal coding. So, while programming can enhance your data analytics skills, it’s not a strict prerequisite.
	How does Data Analytics skill boost my career?
	Data Analytics skills can significantly boost your career as these skills are in high demand across various industries. These skills can help you make informed decisions, predict trends, and improve business strategies. This inturn can make you a valuable asset to any organization, opening up opportunities for promotions and higher compensation. Additionally, the demand for data analytics skills spans across various industries, providing a wide range of career options.
	What is Data Analytics?
	Data Analytics is the process of examining, cleaning, transforming, and modeling data with the goal of discovering useful information, drawing conclusions, and supporting decision-making. It involves a range of techniques and methodologies to analyze data from various sources in different formats, both structured and unstructured. Data Analytics allows us to make sense of vast amounts of data to help businesses and organizations make better decisions and predictions.
	What are the differences between Data Science and Data Analytics?
	Data Science and Data Analytics are two distinct fields, though they overlap in many areas. Data Science is a broader field that includes various techniques such as machine learning, predictive modeling, and statistics. It involves extracting insights from various forms of data, both structured and unstructured. Data Science is more focused on designing and constructing new processes for data modeling and production using prototypes, algorithms, predictive models, and custom analysis. On the other hand, Data Analytics is more focused on processing and performing statistical analysis on existing datasets. It is used to discover useful information and aid in business decision making. Data Analytics tends to be more business and process-oriented. In summary, while both fields involve working with data, the main difference lies in the scope and focus of what they do with this data.
	What are the salaries of a Data Analytics professional in India and US?
	The salaries of a Data Analytics professional can vary widely depending on the industry, location, and level of expertise. In India, the average salary for a Data Analyst is around ₹6,72,500 per year. If a person has experience of 2-3 years then the figure may increase up to ₹6-8 LPA. However, the salary can go up to as much as INR 22 LPA. In the United States, the average salary for a Data Analyst is around $67,063 per year. However, the salary can range from $49,000 to $92,000 per year, and can even go up to $120,591 per year depending on the level of expertise and the state they are working in. Please note that these figures are averages and actual salaries can vary. It’s also important to note that salaries are often accompanied by other forms of compensation such as bonuses, profit sharing, and benefits. SOURCE: Payscale.com and Indeed.com
	What is the career path of a Data Analytics professional?
	The career path of a Data Analytics professional can vary based on individual goals and interests, but a typical path might look like this: Entry-Level Positions: After obtaining a relevant degree, one might start as a Data Analyst or Junior Data Analyst, where the primary tasks involve processing and making sense of large datasets. Mid-Level Positions: With a few years of experience, one could move into roles such as Senior Data Analyst, Data Scientist, or Data Engineer, where the tasks might involve more complex data analysis, predictive modeling, and machine learning. Management and Beyond: After gaining substantial experience and expertise, one could aim for leadership roles such as Data Analytics Manager, Director of Data Science, or even Chief Data Officer. These roles involve overseeing data strategies and policies in an organization.
	How does this "Data Analytics and AI course" differ from a traditional data analytics course?
	This course goes beyond traditional data analytics by integrating cutting-edge AI techniques. You'll learn not just to analyze historical data, but also to leverage AI for automated data preparation, intelligent insights, predictive modeling using AutoML, and even building practical AI applications, setting you apart in the job market.
	What specific AI tools and technologies will I learn in this course?
	Our Data Analytics and AI course covers practical AI tools and libraries such as PyOD, KNN Imputation, Recordlinkage, featuretools, SMOTE for data preparation; Sweetviz for intelligent EDA; Microsoft Azure AutoML for predictive analytics; and how to build applications using leading AI APIs (OpenAI, Gemini via Python) and low-code platforms like n8n or Power Apps.
	Do I need a strong coding background for the AI modules in this course?
	No, this Data Analytics and AI course is designed for beginners. While there's minimal exposure to Python for practical application of AI libraries, the focus is on using AI tools and platforms (like AutoML, AI APIs, and low-code solutions) rather than deep programming or complex algorithm development. We emphasize practical application over theoretical programming.
	How will learning AI in this course boost my career as a Data Analyst?
	Adding AI skills to your data analytics expertise makes you a 'future-proof' analyst. You'll be able to automate tedious tasks, extract deeper and faster insights, build more accurate predictive models, and even develop intelligent applications, making you highly valuable to employers looking for advanced, efficient data professionals.
	What kind of AI applications will I be able to build after completing this course?
	You will gain the skills to build practical AI applications like sentiment analyzers for customer reviews, text summarizers, marketing copy generators, sales forecasting apps, and even image recognition tools, often by leveraging powerful AI APIs and low-code platforms.

	Aspiring Data Analysts & Business Analysts
	Professionals from non-tech backgrounds looking to break into analytics
	Business professionals seeking to upskill with AI + Data skills
	Students and freshers with strong interest in analytics and AI
	Teams undergoing upskilling in corporate environments

	Course Overview
1	Course Learning Objectives
2	Syllabus
3	Data Analytics Certification (CBDA)
4	Take-aways
	Introduction to Data Analytics
1	What is Data Analytics?
2	Importance of Data Analytics
3	Key Elements of Data Analytics
4	Opportunities and Careers paths in Data Analytics
5	Roles and responsibilities of a Data Analyst
	Types of Data Analytics
1	Descriptive Data Analytics
2	Predictive Data Analytics
3	Diagnostic Data Analytics
4	Prescriptive Data Analytics
5	Quiz
	SQL for Sourcing Data
	SQL Overview Data Query Language Data Manipulation Language Accessing Data from Multiple Tables
2	Class Exercises
	Data Visualization with Power BI
	Fundamentals of Data Visualization Introduction to Power BI Data Visualization using Power BI Slicers and Filters in Power BI Interactive Visualisations in Power BI Creating Paginated Report Creating Dashboards in Power BI
	Phase I - Identify the Research Questions
	Define Business Problem or Opportunity. Identify and Understand the Stakeholders, Assess Current State, Define Future State, Formulate Research Questions, Plan Business Data Analytics Approach, and Select Techniques for Identify the Research Questions
	Phase II - Source Data
	Plan Data Collection, Determine the Data Sets, Collect Data, and Validate Data.
	Phase III - Analyze Data (Cleaning, Transformation and Analyzing data)
	Develop Data Analysis Plan, Prepare Data, Explore Data, Perform Data Analysis, and Assess the Analytics and System Approach Taken
	Phase IV - Interpret and Report Results
	Validate Understanding of Stakeholders, Plan Stakeholder Communication, Determine Communication Needs of Stakeholders, Derive Insights from Data, and Document and Communicate Findings from Completed Analysis, and Select Techniques for Interpret and Reporting Results
	Phase V - Use Results to Influence Business Decision-Making
	Recommend Actions, Develop Implementation Plan, and Manage Change.
	Phase VI - Guide Organizational-Level Strategy for Business Data Analytics
	Organizational Strategy, Talent Strategy, and Data Strategy.
	LLM
	LLM Models Tokenization Fine Tuning GAN RAG
	Machine Learning Basics
	What is Machine Learning NLP Neural Network Supervised Learning Linear (Continuous) Regression Categorical Classification Unsupervised Learning Recommendations Clustering Dimensional Reduction
	Artificial Intelligence and Generative AI Fundamentals
	What are AI and Generative AI How does it work? How can you use for better efficiency More use cases
	Leveraging AI for Enhanced Data Analytics
	AI-Powered Data Preparation & Augmentation Intelligent Exploratory Data Analysis (EDA) Applied Predictive Analytics Generative AI for the Data Analyst
	Learning to Develop Practical AI Applications
	Building with AI APIs Low-Code/No-Code AI App Development

	No, if you don’t like or are not good at programming, this course is designed for you.
	This course will not require you to learn programming skills in depth, minimal exposure to programming is required.

	Data Analytics Fundamentals
	Descriptive Analytics
	Diagnostic Analytics

	SQL for Data Extraction
	Power BI for Business Dashboards
	AI-Powered Data Preparation

	Intelligent EDA & Auto Insights
	AutoML Platforms
	Model Interpretation

Batch starts	Batch Timing	Early bird discount 10%	Offer Code Expires in
Batch Start Date and Time	Early Bird Discount and Validity
02 August 2025 02 August 2025 Sat - Sunday 07:30 AM - 10:30 AM (IST)	Sat - Sunday 07:30 AM - 10:30 AM (IST)	10% off (Valid Till 12th Jul) 10% off (Valid Till 12th Jul) Use code DTAN2313	12 July 2025 23:59:59	10% off (Valid Till 12th Jul) 10% off (Valid Till 12th Jul)	Use code DTAN2313	12 July 2025 23:59:59
11 October 2025 11 October 2025 Sat - Sunday 07:30 AM - 10:30 AM (IST)	Sat - Sunday 07:30 AM - 10:30 AM (IST)	10% off (Valid Till 21st Sept) 10% off (Valid Till 21st Sept) Use code DTAN2314	21 September 2025 23:59:59	10% off (Valid Till 21st Sept) 10% off (Valid Till 21st Sept)	Use code DTAN2314	21 September 2025 23:59:59
20 December 2025 20 December 2025 Sat - Sunday 07:30 AM - 10:30 AM (IST)	Sat - Sunday 07:30 AM - 10:30 AM (IST)	10% off (Valid Till 30th Oct) 10% off (Valid Till 30th Oct) Use code DTAN2323	30 October 2025 23:59:59	10% off (Valid Till 30th Oct) 10% off (Valid Till 30th Oct)	Use code DTAN2323	30 October 2025 23:59:59

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Data Analytics & AI Course: Future Proof Your Analyst Career

Data Analytics Certification Course Overview

Key Features of Our Data Analytics & AI Certification Course

AI-Integrated Curriculum

Hands-On Projects with Business Use Cases

Master Tools Like SQL, Power BI & No-Code AI Platforms

Generative AI for Analysts

Live Online Classes + Mentorship

Career Support & Certification

Who Should Enroll in Data Analytics Certification Courses?

Do I need to learn programming?

Skills Covered

The Value of Data Analytics Certification Courses: Are They Worth It?

Data Analytics Market

Top Employers

Course Testimonials

Elzahn Oosthuizen

CBDA Certified

Senior Specialist: Business Analyst at Altron Systems Integration City of Cape Town, South Africa

A.N.M. Shibly

CBDA Certified

Chief Operating Officer at Avis Technologies Limited Bangladesh

Soham K

CBDA Certified

Data analytics Consultant at Integrated Learning Systems(ILS) India

Course Curriculum

Trainer and Mentor

Data Analytics Certification Courses: Syllabus Details

Class and Course Projects

Course Certificate

Data Analytics Fees

₹ 31,410

₹ 34,900

$ 482

$ 535

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Upcoming Batches

Sat - Sunday

07:30 AM - 10:30 AM (IST)

Sat - Sunday

07:30 AM - 10:30 AM (IST)

10% off (Valid Till 12th Jul)

10% off (Valid Till 12th Jul)

Use code DTAN2313

10% off (Valid Till 12th Jul)

10% off (Valid Till 12th Jul)

Use code DTAN2313

Sat - Sunday

07:30 AM - 10:30 AM (IST)

Sat - Sunday

07:30 AM - 10:30 AM (IST)

10% off (Valid Till 21st Sept)

10% off (Valid Till 21st Sept)

Hands-On Projects with
Business Use Cases

Master Tools Like SQL, Power
BI & No-Code AI Platforms

Live Online Classes +
Mentorship

Senior Specialist: Business Analyst at Altron Systems Integration
City of Cape Town, South Africa

Chief Operating Officer at Avis Technologies Limited
Bangladesh

Data analytics Consultant at Integrated Learning Systems(ILS)
India

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