Data Science AI & ML

Data Science Courses

Data Science 

Chools offers Data Science course, the most comprehensive one in the market, covering the complete Data Science lifecycle concepts from Data Collection, Data Extraction, Data Cleansing, Data Exploration, Data Transformation, Feature Engineering, Data Integration, Data Mining, building Prediction models, Data Visualization and deploying the solution to the customer. Text Mining, Regression Modelling, Hypothesis Testing, Predictive Analytics, Machine Learning, Deep Learning, Neural Networks, Natural Language Processing, Predictive Modelling, R Studio, Tableau, Spark, Hadoop, programming languages like R programming, Python are covered extensively as part of this Data Science training.

Who Should Do the Data Science Course?

  Professionals who can consider a Data Science course as the next logical move to enhance their careers include:

  Professional from any domain who has logical, mathematical, and analytical skills

  Professionals working on Business intelligence, Data Warehousing, and reporting tools

  Statisticians, Economists, Mathematicians

  Software programmers

  Business analysts

  Six Sigma consultants

  Fresher from any stream with good Analytical and logical skills

Why One Should Take The Data Science Course?

Is Data Science certification being worth pursuing as a career?

The answer is a big YES for myriad reasons. Digitalization across the domains is creating tons of data and the demand for Data Science professionals who can evaluate and extract meaningful insights is increasing and creating millions of jobs in the space of Data Science. There is a huge void between the demand and supply and thereby creating ample job opportunities and salaries. Data Scientists are considered to be the highest in the job market. The Data Scientist career path is long-lasting and rewarding as the data generation is increasing by leaps and bounds and the need for Data Science professionals will increase perpetually.

➤    4 Lakh jobs are vacant in Data Science, Artificial Intelligence and Big Data roles according to NASSCOM

➤    The world will notice a deficit of 2.3 Lakh Data Science professionals by 2021

➤    The Demand for Data Scientist professionals has increased by 417% in the year 2018, in India, as per the Talent Supply Index

➤    Data Science is the best job to pursue according to Glassdoor 2018 rankings

➤    Harvard Business Review stated that ‘Data Scientist is the sexiest job of the 21st century

Why Should You Choose Chools for Data Science,

If you are serious about a career pertaining to Data science, then you are at the right place. Our expert trainers will help you with upskilling the concepts, to complete the assignments and live projects.

Why Chools Is the Best Data Science Training Institute

Chools offers the best Data Science certification online training along with classroom and self-paced e-learning certification courses. The complete Data Science course details can be found in our course agenda on this page.

What Is Data Science? Who Is Data Scientist?

Data Science is all about mining hidden insights of data pertaining to trends, behavior, interpretation, and inferences to enable informed decisions to support the business. The professionals who perform these activities are said to be Data Scientists / Science professionals. Data Science is the most high-in-demand profession and as per Harvard and the most sort after a profession in the world. No wonder the Data Scientist course is one of the most sought after

You May Question If Data Science Certification Is Worth It?

The answer is yes. Data Science / Analytics creating myriad jobs in all the domains across the globe. Business organizations realized the value of analyzing historical data in order to make informed decisions and improve their business. Digitalization in all walks of the business is helping them to generate the data and enabling the analysis of the data. This is helping to create myriad data science/analytics job opportunities in this space. The void between the demand and supply for Data Scientists is huge and hence the salaries pertaining to Data Science are sky high and considered to be the best in the industry. The Data Scientist career path is long and lucrative as the generation of online data is perpetual and growing in the future.

Course Description

Artificial Intelligence (AI)

Artificial Intelligence (AI) is the next big thing in the technology field and a large number of organizations are already implementing AI and the demand for professionals in AI is growing at an amazing speed. Artificial Intelligence (AI) course with Chools will provide you with a wide understanding of the concepts of Artificial Intelligence (AI) to make computer programs to solve problems and achieve goals in the world.

What Is Artificial Intelligence (AI)?

Artificial Intelligence (AI) is the next big thing in the technology field and a large number of organizations are already implementing AI and the demand for professionals in AI is growing at an amazing speed. Artificial Intelligence (AI) course with Chools will provide you with a wide understanding of the concepts of Artificial Intelligence (AI) to make computer programs to solve problems and achieve goals in the world.

Applications Of AI?

Siri, Alexa and other smart assistants

Self-driving cars


Conversational bots

Email spam filters

Netflix’s recommendations

What Are The Course Objectives?

Artificial Intelligence (AI) is becoming smarter day by day in all business functions to elevate performances. AI is used widely in gaming, media, finance, robotics, quantum science, autonomous vehicles, and medical diagnosis. AI technology is a crucial prerequisite in much of the digital transformation taking place today as organizations position themselves to capitalize on the ever-growing amount of data being generated and collected.

To build a successful career in Artificial Intelligence (AI), this course is intended to give a complete understanding of Artificial Intelligence concepts. This course enables you to get practical, hands-on experience to ensure a hassle-free execution of real-life projects. This AI course leverages world-class industry expertise in making you professional data science experts.

Chools familiarizes you with the basic terminologies, problem-solving, and learning methods of AI and also discuss the impact of AI

What Skills Will You Learn?

In this Artificial Intelligence (AI) course, you will be able to

➤    Understand the basics of AI and how these technologies are re-defining the AI industry

➤    Learn the key terminology used in AI space

➤    Learn major applications of AI through use cases

Who Should Take This Course?

Chools course on Artificial Intelligence (AI) gives you the basic knowledge of Artificial Intelligence.

This course doesn’t need any programming skills and is best suited for

➤    Management and Non-technical participants

➤    Students who want to learn Artificial Intelligence

➤    Newbies who are not familiar with AI or its implications

Course Description

Machine Learning (ML)

Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values. ML is one of the most exciting technologies that one would have ever come across. As it is evident from the name, it gives the computer that makes it more similar to humans: The ability to learn. Machine learning is actively being used today, perhaps in many more places than one would expect.

Applications Of  ML?

  • Web Search Engine:One of the reasons why search engines like google, bing, etc work so well is because the system has learned how to rank pages through a complex learning algorithm.
  • Photo tagging Applications:Be it Facebook or any other photo tagging application, the ability to tag friends makes it even more happening. It is all possible because of a face recognition algorithm that runs behind the application.
  • Spam Detector:Our mail agent like Gmail or Hotmail does a lot of hard work for us in classifying the emails and moving the spam mails to the spam folder. This is again achieved by a spam classifier running in the back end of the mail application.

What Are The Course Objectives?

To understand the basic theory underlying machine learning.

To be able to formulate machine learning problems corresponding to different applications.

To understand a range of machine learning algorithms along with their strengths and weaknesses.

To be able to apply machine learning algorithms to solve problems of moderate complexity.

To apply the algorithms to a real-world problem, optimize the models learned and report on the expected accuracy that can be achieved by applying the models.

What Skills Will You Learn?

Students will be able to appreciate the importance of visualization in the data analytics solution

They can apply structured thinking to unstructured problems

Understand a very broad collection of machine learning algorithms and problems

Learn algorithmic topics of machine learning and mathematically deep enough to introduce the required theory

They can develop an appreciation for what is involved in learning from data.

Who Should Take This Course?

Better Career Opportunities and Growth.

Better Salaries.

Lack of Machine Learning Skills is Plaguing Corporations.

Machine learning and Data Science are intricately linked.

Machine learning evolves from artificial intelligence and the study of pattern recognition. Today, when excessively huge amounts of data are being dealt with every day, rather than every moment, pattern recognition is something that helps large corporations and websites work magnificently with the users.

Under machine learning, as the name suggests, the machine learns from a given set of algorithms and intensive as well as extensive test cases. Based on user choices, the e-shopping sites or social networking sites display ‘recommendations’, which is all a part of machine learning, and is quite a revolution these days. So you definitely don’t have to worry about the career choices.


1. Data science is the process of diverse set of data through?

  • A. Organizing data
  • B. Processing data
  • C. Analysing data
  • D. All of the above

Answer: Option D

2. Point out the correct statement.

  • A. Raw data is original source of data
  • B. Preprocessed data is original source of data
  • C. Raw data is the data obtained after processing steps
  • D. None of the above

Answer: Option A

3. How do we perform Bayesian classification when some features are missing?

  • A. We integrate the posteriors probabilities over the missing features
  • B. We ignore the missing features
  • C. We assuming the missing values as the mean of all values
  • D. Drop the features completely

Answer: Option A

4. The modern conception of data science as an independent discipline is sometimes attributed to?

  • A. John McCarthy
  • B. Arthur Samuel
  • C. William S.
  • D. Dennis Ritchie

Answer: Option C

5. In Bootstrapping Procedures of Bagging Algorithm, which is TRUE?

  • A. Default: Choose all observations and features
  • B. Random Subspace: All observations but subsets of features
  • C. Random Patches: Subset of features and subsets of observation
  • D. All of the above

Answer: Option D

6. Which of the following is FALSE related to Feature Selection Techniques?

  • A. Filter based methods: Correlation, Anova, Chi-square test
  • B. Wrapper based methods: Forward selection, backward elimination, Stepwise selection
  • C. Embedded methods: LASSO, Ridge, Elastic Net
  • D. None of the above

Answer: Option D

7. Which is one of the significant data science skills?

  • A. Statistics
  • B. Data Visualization
  • C. Machine Learning
  • D. All of the above

Answer: Option D

8. A method used to make vector of repeated values?

  • A. read()
  • B. data()
  • C. rep()
  • D. view()

Answer: Option B

9. Which of the following step is performed by data scientist after acquiring the data?

  • A. Data Replication
  • B. Data Integration
  • C. Data Cleansing
  • D. All of the Mentioned

Answer: Option C

10. K- nearest neighbor’s algorithm is based on ______ learning

  • A. Supervised
  • B. Unsupervised
  • C. Association
  • D. Correlation

Answer: Option B

11. Which of the following statement is true?

  • A. The nature of our business problem determines how outliers are used
  • B. Outliers is a data point that is significantly close to other data points
  • C. Outliers should be identified and removed always from a dataset
  • D. Outliers can never be present in the testing dataset

Answer: Option A

12. Which of the following function gives information about top level data?

  • A. tail
  • B. summary
  • C. head
  • D. None of the above

Answer: Option B

13. Which of the following approach should be used if someone can’t fix the variable?

  • A. Non-stratify it
  • B. Randomize it
  • C. Generalize it
  • D. None of the above

Answer: Option B

14. Which of the following is good way of performing experiments in data science?

  • A. Measure variability
  • B. Generalize to the problem
  • C. Have Replication
  • D. All of the above

Answer: Option D

15. Choose the correct option for residuals in linear regression?

  • A. Residuals are horizontal offset, and the sum of residuals varies between [0,1]
  • B. Residuals are horizontal offset, and the sum of residuals can be unity
  • C. Residuals are vertical offset, and the sum of residuals is always unity
  • D. Residuals are vertical offset, and the sum of residuals is always zero

Answer: Option D

16. CNN is mostly used for which type of data

  • A. Structured Data
  • B. Unstructured Data
  • C. Both Structured and Unstructured
  • D. None of the above

Answer: Option B

17. Which of the following transforms can be performed with data value?

  • A. log10
  • B. cos
  • C. log2
  • D. All of the above

Answer: Option D

18. Which of the following SGD variants is based on both momentum and adaptive learning?

  • A. Adagrad
  • B. RMSprop
  • C. Adam
  • D. Nesterov

Answer: Option C

19. What is the work of a Data Architect?

  • A. Utilize large data sets to gather information that meets their company’s needs
  • B. Work with businesses to determine the best usage of the information yielded from data
  • C. Build data solutions that are optimized for performance and design applications
  • D. All of the above

Answer: Option C

20. A perfect negative correlation is signified by ____________

  • A. 1
  • B. -1
  • C. 2
  • D. 0

Answer: Option D

Frequently Asked Questions

What is Data Science?

We have more data than ever before. But data alone cannot tell us much about the world around us. We need to interpret the information and discover hidden patterns. This is where data science comes in. Data science uses algorithms to understand raw data. The main difference between data science and traditional data analysis is its focus on prediction. Data science seeks to find patterns in data and use those patterns to predict future data. It draws on machine learning to process large amounts of data, discover patterns, and predict trends. Data science includes preparing, analysing, and processing data. It draws from many scientific fields, and as a science, it progresses by creating new algorithms to analyse data and validate current methods.

What does a data Scientist do?

Data Scientists use machine learning to discover hidden patterns in large amounts of raw data to shed light on real problems. This requires several steps. First, they must identify a suitable problem. Next, they determine what data are needed to solve such a situation and figure out how to get the data. Once they obtain the data, they need to clean the data. The data may not be formatted correctly, it might have additional unnecessary data, it might be missing entries, or some data might be incorrect. Data Scientists must, therefore, make sure the data is clean before they analyse the data. To analyse the data, they use machine learning techniques to build models. Once they create a model, they test, refine, and finally put it into production.

What are the Most Popular Coding Languages for data science?

Python is the most popular programming language for data science. It is a universal language that has a lot of libraries available. It is also a good beginner language. R is also popular; however, it is more complex and designed for statistical analysis. It might be a good choice if you want to specialize in statistical analysis. You will want to know either Python or R and SQL. SQL is a query language designed for relational databases. Data scientists deal with large amounts of data, and they store a lot of that data in relational databases. Those are the three most-used programming languages. Other languages such as Java, C++, JavaScript, and Scala are also used, albeit less so. If you already have a background in those languages, you can explore the tools available in those languages. However, if you already know another programming language, you will likely be able to pick u

Why Should I Sign up for this Data Science and AI Certification Course?

The Advanced Certification in Data Science and AI course is conducted by leading experts, who will make you proficient in these fields through online video lectures and projects. They will help you gain in-depth knowledge in Artificial Intelligence and Data Science, apart from providing hands-on experience in these domains through real-time projects.

After completing the course and successfully executing the assignments and projects, you will gain an Advanced Certification in Data Science and Machine Learning from Intellipaat and IIT Madras which will be recognized by top organizations around the world. Also, our job assistance team will prepare you for your job interview by conducting several mock interviews, preparing your resume, and more.

What Are The Prerequisites For This Data Science Training?

T• As such, there is no prerequisite for undertaking this training. However, it is highly desirable if you possess the following skills sets

o Mathematical and Analytical expertise
o Good critical thinking and problem-solving skills
o Technical knowledge of Python, R and SAS tools
o Communication skills

What Are The Career Opportunities For Data Science Professionals?

The Advanced Certification in Data Science and AI course is conducted by leading experts, who will make you proficient in these fields through online video lectures and projects. They will help you gain in-depth knowledge in Artificial Intelligence and Data Science, apart from providing hands-on experience in these domains through real-time projects.

o Data Analyst
o Research Analyst
o Data Scientist
o Data Analyst
o Big Data Analytics Specialist
o Business Analyst Consultant / Manager

Who should learn data science?

To master this exciting technology, all you need to have is a passion for pursuing it. Considering the demand of this technology, many youngsters and many existing working professionals are seeking a career in this technology. As many institutes are offering several courses in this domain, you could register yourself for the program that fits your specifications. You must be enthusiastic about learning the tools and techniques of Data Science. Being good at subjects like Mathematics, Statistics, Programming languages, Data Visualisation tools would be add ons to gain expertise in this domain.

Is doing Data Science worth it?

Yes, it is entirely worth it to learn Data Science and choose it as your career.

What are the prerequisites to start a career in Data Science?

Considering this soaring demand in Data Science and Data Analytics, if you want to learn Data Science online, some Data Science prerequisites are as follows:

Mathematical Skills: One must be good at mathematical concepts, such as linear algebra, matrices, calculus, gradients, etc. is considered as one of the major prerequisites for taking up Data Analytics courses.

Programming Skills: Having a concise knowledge of programming, such as Python, C, C++, SQL, Java, etc., would help you gain complete knowledge and understanding throughout the Data Science online course.


Data Processing: As Data Science is all about dealing with data, an individual must be familiar with data mining, data modeling, data processing, etc., which makes it easy for you to pursue Data Science online training.

Statistical Analysis: Being good with statistical analysis would be a great asset to learning Data Science. Data Science aims to extract valuable insights from a vast collection of data. Experience working with analytical tools such as Hadoop, R, SAS, and many more, will serve you in efficiently performing the statistical analytics of the given data.

Data Visualization Skills: Knowing the data visualization tools such as Matplottlib, Tableau, and many more would benefit you in comprehending the complex outcomes and letting the audience understand the metrics.

What is a data scientist's job?

A data scientist’s job is to collect, clean, and analyze data to find trends and insights. They use their skills in statistics, programming, and machine learning to build models and algorithms to optimize decision-making. Data scientists also communicate their findings to others through reports and presentations. Data scientists work in multiple industries, including healthcare, finance, technology, and retail. They utilize their skills to solve business problems and help organizations make more informed decisions

Data scientists typically have a background in computer science, statistics, and mathematics. A data scientist’s job is to make sense of data. They use their skills in statistics, computer science, and mathematics to clean, organize, and analyze data. Data scientists also develop algorithms to help make decisions based on data.

What challenge projects can I make?

Popular projects include, but aren’t limited to:
Python – analyze U.S. Medical Insurance costs
Data Visualization – analyze a plot data about GDP and life expectancy
Data Analysis – interpret data about the endangered animals for the National Park Service
Machine Learning – test predictions you draw about data
Final Project of a topic of your choice!

Are there any prerequisites to learn the Data Science course?

One need not have any major knowledge in Data Science. A basic understanding of technology is all enough to get started. It is better to possess knowledge of mathematical and communication skills, Python, R, and SAS tools.

What are my takeaways after completion of the Data Science course?

Based on the program you choose, you will get a course completion certificate from Innomatics. Mastery level certification from Chools

What are the career opportunities in Data Science Technology ?

)As data has become the never-ending part of this world, businesses need people to work with data for effective business processing. Organizations are ready to recruit and pay top dollars to the right dollars, which can leverage the business.

Here are some of the roles you can find in Data Science

Research Analyst
Data Scientist
Data Analyst
Big Data Analytics Specialist
Business Analyst Consultant / Manager
Data analyst

What is the eligibility criteria to learn Data Science course?

Anyone who has a bachelor’s degree, a passion for data science, and little knowledge of it are eligibility criteria for the Data Science Course.

Why this program?

Most of the existing programs do not integrate all three aspects (Data Science, Machine Learning, Artificial Intelligence) for you to become a practitioner in the field. Additionally, nearly none of the other programs cover what this program does, what we like to call the edges of Data Science – from data engineering to deployment and prototyping.
Most programs out there only focus on building models in the development environment, which is not of much value when it comes to implementation. This program is deeply integrated with the Data Science Program from MIT, which provides you with deep knowledge that is unparalleled in online or in-person learning. This is augmented by extensive hands-on industry case studies, real-life data sets, Big Data Engineering, and MLOps.

Does the job Assistance Program Gurantee me a job?

Apparently, no. Our job assistance program is aimed at helping you land in your dream job. It offers a potential opportunity for you to explore various competitive openings in the corporate world and find a well-paid job, matching your profile. The final decision on hiring will always be based on your performance in the interview and the requirements of the recruiter.

Is it Possible to switch from self-paced training to instructor-led training?

You can definitely make the switch from self-paced training to online instructor-led training by simply paying the extra amount. You can join the very next batch, which will be duly notified to you.

What is Qualification required to enroll for a Data Science Course?

You can definitely make the switch from self-paced training to online instructor-led training by simply paying the extra amount. You can join the very next batch, which will be duly notified to you.

How do Data Scientists use statistics?

Statistics plays a powerful role in Data Science. It is one of the most important disciplines to provide tools and methods to find structure in and to give deeper insight into data. It serves a great impact on data acquisition, exploration, analysis, validation, etc.

Between R and Python, Which one would you choose for text analysis?

Between R and Python, Python would be the best choice as it has Pandas library which provides high-performance data analysis tools and easy to use data structures. However, you can go with either of these languages depending on the complexity of the data which is being analysed.

Can a data science major pursue a computer science minor?

No, we do not allow data science students to minor in computer science, as the minor coursework is essentially built into the major.

What could students learn in the data science program from the electrical engineering technical elective courses?

Many of the rudiments of data science have close ties to electrical engineering methods. For example, stochastic gradient descent methods are at the heart of the Least Mean Squares (LMS) method in adaptive filtering (covered in EE 5542). Convolution operations, central to convolutional neural networks, are foundational in linear system theory (covered in EE 2015, 3015, 4541, 5545, 5549), controls (EE 4231, 4233, 5231, 5235, 5721), image processing (EE 5561), and communication systems (EE 4501, 4505, 5501, 5505). Nonlinear optimization (EE5239) also finds applications in signal processing, communications, image processing, power systems, and electromagnetics, to name just a few areas. In this sense, these courses would provide students in the data science program with additional historical perspective and a broader range of application domains for the techniques they would learn in the program.

What's the difference between data science and industrial and systems engineering? Why would I choose to study one over the other?

Industrial and Systems Engineering (ISyE) is about using engineering thinking to design and operate systems that are efficient, cost-effective, reliable and safe. The focus is on identifying the key levers of the systems that can achieve these goals and on developing a variety of tools to set them in the best possible way.

Because systems can involve a variety of components, including machines and humans, and often operate in market environments, ISyE draws not only engineering but also on business and data science; data being crucial in creating the very models from which recommendations for improvement/optimal operation and design can be derived.

The focus of data science is focused specifically on learning from data; data manipulation, summarization, visualization, storage, and drawing insights from data. The focus of ISyE is on the design and efficient operation of large-scale systems, e.g. transportation, supply chain, finance, etc. Both data science and ISyE employ methodologies to learn from data and to help people make better decisions.

Is the program available online?

The program is available online to working students who are enrolled in the program in the United States or Canada, though not all electives will be available.