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.

Data Analytics Certification Course | Data Analytics Training

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	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?
	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?
	It’s a vast field so you have to start with assessing your aptitude. Are you from a programming field or non-programming field. Based on this assessment, you can choose the specific field for Data Analytics. 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.

	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.
	Data Architecture basics with Microsoft Azure
	Azure Cloud platform ADF (pipelines) ADB Ingestion & Curation Layer
	Machine Learning Basics
	What is Machine Learning Supervised Learning Linear (Continuous) Regression Categorical Classification Unsupervised Learning Recommendations Clustering Dimensional Reduction
	Artificial Intelligence and Generative AI
	What are AI and Generative AI How does it work? How can you use for better efficiency More use cases

	Experienced Business Analysts, who wish to upskill and get into Data Analytics Projects
	Team Leaders, Project leaders and Project Managers who wish to upskill and wish to manage Data Analytics Projects
	A data analyst who wishes to learn all the data analytics skills

	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
	Machine Learning
	Machine Learning Algorithms

	Descriptive Analytics
	Predictive Analytics
	Diagnostic Analytics

	Trend Analysis
	Azure Fundamentals
	Case Studies and Course Projects

Core Business Analysis

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Data Analytics Certification Training

Data Analytics Certification Training

Who can do this certification course?

Do I need to learn programming?

Is Data Analytics certificate worth it?

Data Analytics Market

Top Employers

Skills Covered

Course Project

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

Know Your Trainer

Mr. Vinayak M Kesari

Sr. Data Analyst

Data Analytics Course Syllabus

Course Certificate

Data Analytics Course Fees

₹ 21,730

₹ 24,145

$ 395

$ 439

Add-ons

₹ 25,245

₹ 28,050

$ 459

$ 510

Add-ons

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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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