SOURCE: Siraj Raval

Data Science is the most demanding fields in today’s world. This field uses scientific methods, processing, algorithms, and system to attract knowledge. It is a multi-disciplinary field and the future of Artificial Science which includes machine learning. The data analyst explains the processing history of data.

We can notice several advertisements implying its importance in the industry and witness promotions to study the course. Let us first understand what data science is before getting into its syllabus.

It requires technical knowledge of various coding languages and awareness of some technologies but the central element of data science is not mere coding or using complicated technologies to make live or visual models.

In short, Data Science is a combination of data mining and computer sciences. It is the study of the impact of data on the company/organization/industry/situation. It is an evolution of the web design arriving at a sophisticated data infrastructure. Websites have evolved from simply displaying pamphlets and brochures to a more interactive system by employing and handling relevant data.

To be a successful data scientist, in-depth knowledge of data structure and data manipulation is warranted. Critical skills including logical, statistical, and technical education become equally important. A decent background in software languages like R, Python, SQL, etc. would be an added advantage.

What is Data Science?

The Data Science Syllabus is not just limited to the assimilation of raw data and structuring it, but also analyzing data that can be both structured and unstructured.

Various tools and algorithms are taught through the course that help is a better understanding of data and therefore, understand the predictive analysis aspect of Data Science. To become an asset in this field, dispensing the algorithms, tools, and associated skills is not an option.

Also Read: What is Data Science

List Of Components in Data Science Syllabus

Here are the key parts of the Data Science Syllabus:

1. Machine Learning & Deep Learning

Machine language is nothing but a conversion of the human-understandable data into machine-interpretable code values. The machine can understand these codes and not explicit programming.

This is achieved using algorithms and sometimes artificial intelligence (AI). Deep learning is a subdivision of machine learning which is more specific and applies algorithms more independently and with much lesser inputs from the user or programmer.

It is built with artificial neural networks and is more prompting by recording data retrieval history by experience. This particular subject focusses on and expects you to be well informed of the different algorithms like linear regression, clustering, logical regression, decision trees, etc. The connection between neural networks, libraries, and algorithms are studied here.

1. Big data technology: All the leading industries handle big data today and require analytics. Big data technologies help to write map produces codes. The most prevalent big data technologies are Hadoop HDFS and Apache Spark.
2. Data ingestion and Data Munging: In data ingestion, data is imported or received through proper channels for being manipulated or analyzed or stored. There are tools for this process – Apache Flume and Apache Corp. Any change that is made using artificial algorithms upon raw data for clearer visualization is data munging. R and Python packages may be used for this. Selection and removal of data are done through analysis and any data discrepancies are corrected.
3. Visualization: The most important part is to organize the analyzed data and present it. No technology or algorithm or code can be shown. An understandable report needs to be submitted. Report creation training and techniques will be learned under this sub-topic.
4. Problem-solving: The ultimate purpose of the entire course is to solve the business problem using data sets. The examined characteristics of the given raw data need to be taken into account to make a final call over the problem. For example, if a certain value in the data is high then the impact of the deviation is analyzed and arrived at a perfect strategical solution to overcome the variance. Hands-on experiences and case studies will be given in the problem-solving section under the data science course.

2. Probability & Statistics

1. Discover what the data is conveying or implying by analyzing it. This is the core part of decision-making. Careful analysis of data to arrive at meaningful solutions to business problems should be done which is achieved by the following systems.
2. Basic mathematics used for the analysis of data like mean, median, mode, standard deviation, variance (measures of average and dispersion).
3. Probability distributions like Poisson distribution, binomial distribution, etc. 
4. Application of various theorems and equations to manipulate the data according to the case (ex: through linear transformation).
5. Calculation of deviation of data from the standardized or normalized value through curve analysis.

3. Programming (Coding)

All data precisely referred, as big data cannot be jotted down on paper or excel sheets every time. It becomes impossible to handle data when it pars a certain fixed volume.

This invites the need for programming languages to fetch a few particular sets of data to be analyzed or manipulated through selection criteria by writing relevant codes. Python, R, and Saas are the most commonly used programming languages in data science.

1. Python: It is a simple and powerful language that is user-friendly. It is platform-independent, which enables usage of python in Linux, Windows, and Mac environments. Python coding is easy to learn as it is similar to the English language and the keywords imply the same English meaning. Python libraries like Expat, Dlib, Apache, and many more have details pertaining to machine learning, medical arrays, and visualization, and they are used to build statistical models for further studies.
2. R: R is an open-source programming and statistical language. It is a very well documented software language thus being easy for learning. It is highly cost-effective and has strong statistical capabilities.
3. Saas: The expansion of Saas is Statistical Analytical System in itself which means that it is designed for performing statistical operations conveniently. The commercial analytics market uses the Saas tool prominently. It comes with an excellent GUI. Being a fourth-generation language, it is suitably designed to accommodate operations in the development of commercial business software. It imbibes many in-built codes in order to reduce coding time and effort. It stands unique from Python and R by being a fourth-generation language.

4. Database knowledge

Having said that programming language is used to fetch and work on the stored data, the data has to be stored somewhere. It obviously cannot fit into the office tools and hence employment of databases comes into the picture.

All companies involving data science sectors in their projects make use of MySQL or Cassandra (NoSQL) databases. The gathered or keyed in or transmitted information is stored in simple and complex tables with unique properties.

Subjects Offered in Data Science Syllabus

There are some core subjects in Data Science Syllabus:

1. Data Science: Introduction and Importance– Data science has gained importance in the business world. In this subject, teachers will teach you about the handling of data which is in the unstructured form to convert it into a structured form.
2. Data scientist roles and responsibilities– Data scientist is responsible for advising business data analysis and go through the statistics of the business data.
3. Data acquisition and data science life cycle– The life cycle of data science needs to be identified first to the person who knows what data to acquire and when to acquire. Data acquisition involves acquiring data from all the internal and external sources and helps to answer the business question.
4. The algorithm used in machine learning– It is an evolution of regular learning algorithms. It makes the program smarter through which you will learn from the data provided.
5. Working on data mining, data structures, and data manipulation– Data mining is a way to find out hidden information from databases, and data structure is a way of organizing data or storing data.

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Additional Data Science Subjects

Before one plan on going ahead with Data Science as a career preference, knowing about the syllabus and subjects under the course is a pre-requisite.

Some of the subjects that are essential to one’s learning experience and fundamentals for the understanding of the course are:

1. Introduction and Importance of Data Science
2. Statistics
3. Working on Data Mining, Data Structures, and Data Manipulation
4. Algorithms used in Machine Learning
5. Data Scientist Roles and Responsibilities
6. Data Acquisition and Data Science Life Cycle
7. Deploying Recommender Systems on Real-World Data Sets 
8. Experimentation, Evaluation, and Project Deployment Tools
9. Predictive Analytics and Segmentation using Clustering 
10. Applied Mathematics and Informatics
11. Working on Data Mining, Data Structures, and Data Manipulation
12. Big Data Fundamentals and Hadoop Integration with R

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Data Science Syllabus Of IIT

Data science is a post-graduation course and is a 2-year program. The eligibility is B. E or B. Tech graduation with 60% aggregate marks. The selection is based on the merit of the qualifying exams. The average range of course fee is 1.40 to 9 lakhs for the 2 years.

There are various subjects in the syllabus of IIT- 

1. Econometrics
2. Programming foundation for data science
3. Data warehousing and data- mining
4. Mathematics for data analytics
5. Machine learning
6. Large-scale graph analytics
7. Empirical research
8. Big data technologies
9. Data analytics lab
10. Advanced data analytics lab

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Data Science Syllabus Of NMIMS

NMIMS is deemed to be university started full-time. Tech data science program. The main aim is to enhance the main latest development of industries and technologies.

1. The faculty team is highly experienced and more dedicated and motivated.
2. There are various subjects like:
   • Data gathering, cleaning
   • SAS Programming
   • Big data technology 
   • Machine learning 
   • Data mining
   • Data science- Business analytics

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Data Science Syllabus For MSc

MSc in data science is a 5-year integrated program. It provides the basics of advanced mathematical tools. This will enhance your business analytics skills as well as artificial intelligence and also improve your growth in the It sector industries.

1. This will help to gain knowledge in computer programming, mathematics, and business analytics.
2. The jobs will be in both conventional and software industries.
3. Scope for the research who wants to be a scientist or teacher.
4. This is the most wanted program in academics and industry.
5. The curriculum is designed according to the industrial needs and latest trends and technologies.
6. Ability to absorb and understand abstract concepts.

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Data Science Syllabus For Python

Python is a language that is best suited for a data scientist and is a powerful open-source language. It was created by Guido Van Rossum in 1989. Python in data science helps your growth in the IT sector.

1. This will prepare you for three main courses for masters that are Statistics, Machine learning, and Spark.
2. This will start with the learning of the basic process of data science.
3. You will learn Python and Jupyter notebooks.
4. An applied understanding of how to manipulate and analyze uncurated datasets.
5. Fundamental statistical analysis and machine learning methods.
6. Visualization of effective results.

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Top colleges offering Data Science

Several courses have been formulated for data science. There are colleges, universities, and even crash courses by institutions. Let us explore some of the colleges within and outside India that offer data science course. Most of them provide placements in esteemed organizations.

India: 

1. Internation Institue of Information Technology (IIIT) – Bangalore
2. Jigsaw Academy – Bangalore
3. Praxis Business School – Kolkata
4. Aegis School of Business – Bangalore
5. IIT Kharagpur in collaboration with IIM Calcutta and Indian Statistical Institute Kolkata – for Data science + Data analytics

Abroad: 

1. University of Hildesheim, Germany
2. NTU, Singapore
3. Australian National University – MS in Data Analytics
4. The University of Sydney – MS in Data Science
5. University of Birmingham

Online degree: 

1. London University offers a 3 years program in data sciences. IBM offers online courses as well.
2. Berkeley School of information – 6 weeks module.

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Data Science Minimum qualification

A minimum qualification of a bachelor’s degree is essential in order to get admitted to a data science course.

Bachelor’s degree in science/engineering/business/administration/commerce/mathematics/statistics with an aggregate of 50% or an equivalent degree is required. A strong basis and expert skill in mathematics are mandatory.

Data Science Course Duration

The duration, of course, ranges from about ten weeks to five years. Each course provider or university offers different durations. A proper data science degree may take about 20 months.

If the candidate is willing for additional effort, the course could be completed by 12 months. There are sort-term courses completing in about 6-10 weeks’ time as well. Professionals willing to gain certifications may find it useful.

Combinations of data science with various other disciplines:

1. AI + data science: This combo is a very powerful tool in the industry since the entire world revolves around using artificial intelligence to a high degree right from mobile phones to detectors. Programs are offered by many universities for this combination.
2. Data science + analytics: Analytics and data science are bound to go hand in hand with each other because of the complementary relationship they possess. Therefore, studying it together ensures higher success in the field.

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Data Science Experience Required

Experience in databases and knowledge in statistics may help you understand the concepts of data science better. However, beginners also can cope up with the course quite well.

Having some experience will be helpful in the earlier completion of the course. Crash courses and 3 months courses can be taken up if you have job experience in the related fields.

Data Science Fees

Every university differs in its fees structure inclusive of term fees, exam fees, books, seminars, projects, labs, etc. Comprehensively, the average estimated cost of the entire course is as below.

• India: Rs. 4,00,000/-
• Abroad: Rs. 10,00,000/-

Scope of a Data Scientist

Data scientists are scarce in the market currently and in much demand as well. Therefore, a course in data science is sure to help you build your career greatly.

Apart from being heroes of the IT industry, a data scientist can excel in the healthcare industry, travel industry, financial institutions, food industry, and many more. Industries handling huge amounts of data are in constant need of data scientists. Data can never be permanent. So, this change keeps the role of data scientists active in all generations.

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Job Description for Data Science

A data scientist is expected to understand and analyze the business problem, develop a strategy by collecting required data and format it using algorithms or techniques employing appropriate tools, and finally make recommendations for solving the issue. All allegations kept forward need to be backed by suitable data.

Data mining along with statistics plays an important role here because it involves the examination of large preexisting databases to generate new data. In simple terms, the conversion of raw data into useful information is used for making further decisions.

Data Science Salary Expectation

The salary of a data scientist may vary from country to country and organization to organization. Overall, the average salary per month can be estimated to be $95000 annually which is the starting salary for a beginner and $128,750 for a senior end career professional. Statistics say it could rise up to $185,000 for an executive-level post.

You may always place higher demands with the salary you draw once you are adept at the skill and gained sufficient experience. You shall be paid well for your demand because of the high and obligatory requirements for data scientists.

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