Master’s in data science course details

Key takeaways:

• Due to the vast employment opportunities and the strong need for qualified and experienced data scientists in India and abroad, many students and professionals are turning to this field, making a master’s in data science one of the most popular courses to take in 2022.

• The programme’s main goals are to give students specialised and advanced skills and training as well as theoretical and practical knowledge about the various approaches to understanding various phenomena concerning the real world, and it also includes specialisation. 

• The duration of the Masters programme in Data Science course is for two years and is divided into four semesters.

Data science is the study of extracting useful information from data using state-of-the-art analytics technologies and scientific principles for business decision-making, strategic planning, and other reasons. Due to data science scope in India, organisations need it more than ever as data science insights help businesses, among other things, improve marketing and sales campaigns and increase operational effectiveness which is taught in data science courses. 

The term data science was first used in the 1960s. (At the time, it was impossible to foresee the enormous volumes of data generated over the following 50 years!) Data science is a discipline that is constantly developing, employing computer science and statistical methods to acquire insights and generate valuable predictions in a variety of industries.

Over time, both the phrase “data science” and the discipline itself have changed. Due to advancements in data collection, technology, and the mass production of data globally in recent years, its popularity has significantly increased. The emergence of programming languages like Python and methods for gathering, analysing, and interpreting data helped make data science the well-liked field it is today.

Significance of data science

Data science has had a significant impact on business. Let’s discuss the main goal of data science applications in businesses and how it contributes to operational security.

• Learn about your clients

Data on your clients may tell you a lot about their behaviours, demographics, hobbies, and aspirations, among other things.

• Boost your security

Companies can use data science to enhance security and safeguard critical information. Learning about data privacy can help your business avoid using or disclosing sensitive information from clients.

• Streamline the manufacturing process

Another way to use data science in business is to find inefficiencies in manufacturing processes. Manufacturing equipment continuously gathers large amounts of data during industrial processes.

• Future market trend prediction

By gathering and analysing data on a larger scale, you can identify emerging trends in your market. 

The significance of the field of data science has various benefits due to this enormous volume of data. The following are some of the benefits:-

• Multiple career options

Due to its popularity, there are many job opportunities in all of its related industries. They include people who work as data scientists, analysts, researchers etc.

• Business advantages

Data science assists businesses in determining when and how their items sell best, ensuring that things are always delivered at the appropriate location and moment. 

• High-paying jobs and career opportunities include

As long as the position of data scientist remains the most desirable, the pay is also very high. The average master’s in data science salary for a data scientist is USD 106,000 annually, according to a Dice salary survey.

How data science works

Working with large amounts of data, making calculations, utilising machine learning, and other activities may all be included in the field of data science. To infer the information from the source, many methods are used, such as data extraction, information preparation, model design, model construction, and many more. 

Let’s quickly go over each procedure.

• Discovery

To begin with, it is crucial to understand the many decisions, requirements and needs of the project. These resources can include people, creativity, time, and information. You must now outline the trade issue and specify the initial hypotheses (IH) to test.

• Information gathering

You want to look into, prepare, and condition data for modelling at this point. You will be able to clean, modify, and visualise information. This will help you identify the exceptions and establish a connection between the variables. 

• Planning a model

Here, you can choose the techniques and tactics for connecting the various components. Exploratory Data Analytics (EDA) can be used with a variety of factual equations and visualisation tools.

• Building a model

You will construct datasets for training and testing at this stage. You can examine several learning techniques, including classification, association, and clustering, and ultimately decide on the best fit method to build the show.

• Operationalising

You distribute the final briefings, codes, and specialised reports at this stage. In addition, a pilot project is presently being implemented in a real-time generation environment. This will help you understand the execution and any other relevant limitations.

• Discuss your results

At this time, evaluating the success of the target is crucial. Therefore, in the final step, you identify all the significant findings, share them with the partners, and evaluate whether the venture’s results are a success or a failure using the criteria established in Stage 1.

Understanding data science through a case study

Using the full lifecycle that we previously mentioned, we will forecast the occurrence of diabetes in this use case. Let’s walk through each step.

Step 1

First, we will gather the information based on the patient’s medical background, as described in Phase 1. You can refer to the sample data below.

Source 

You can see that we have the many qualities listed below.

Attributes:

npreg     –   Number of times pregnant

glucose   –   Plasma glucose concentration

bp          –   Blood pressure

skin        –   Triceps skinfold thickness

bmi        –   Body mass index

ped        –   Diabetes pedigree function

age        –   Age

income   –   Income

Step 2:

• We now need to prepare the data for data analysis after we receive it.
• This data has to be cleaned up because it contains numerous errors such as missing values, blank columns, abrupt values etc.
• Here, the data has been arranged into a single table under several properties.
• Let’s look at the example data listed below.

There are many inconsistencies in this data.

• One is written in words in the column npreg, whereas it should be written as a number, like 1.

• One of the values in column bp is 6600, which is impossible (at least for humans) because bp cannot increase to such an enormous value.

•  The table should be cleared.

• We will clean and preprocess this data by eliminating the outliers. If you recall, data preparation is the second stage of our process.

• Finally, we have cleaned the data that can be used for analysis, as demonstrated below.

Step 3:

Let’s now conduct some analysis, as was discussed in Phase 3.

The data will first be loaded into the analytical sandbox, where several statistical functions will be applied to it. 

Then, to acquire a good picture of the distribution of the data, we employ visualisation techniques like histograms, line graphs, and box plots.

Step 4:

The decision tree is now the greatest fit for this type of problem based on the insights gained from the previous stage. We’ll see how.

Since we already have the key analytical features, we will use supervised learning to create a model in this case. In addition, decision trees are particularly useful since they assess all attributes. In our situation, the link between npreg and age is linear, whereas the relationship between npreg and ped is nonlinear.

• Because we may create numerous trees using different combinations of features and then implement the one with the highest efficiency, decision tree models are also quite robust.

Check out our decision tree now.

Source 

The glucose level is the most crucial variable in this case, making it our root node. The current node and its value determine the next crucial parameter to be taken. The process continues until a positive or negative outcome is obtained. 

Step 5:

We will conduct a modest trial project at this phase to determine whether our results are appropriate. We’ll also check for any performance restrictions. 

Step 6:

We will share the output for deployment once the project has been completed.

A data scientist primarily needs expertise in the three areas listed below.

As the figure above shows, you must develop various hard and soft talents. To analyse and visualise data, you must have a strong background in mathematics and statistics. Machine Learning is the foundation of data science, and you must be proficient in it. To properly understand the business difficulties. 

Masters in data science 

A master’s in data science is a two-year full-time postgraduate programme focusing on Calculus, Descriptive Statistics, and Programming to comprehend the various phenomena with a large set of real-world data. Follow the table below for an overview of the data science course details.

Data Science course details

Course name	M.Sc. in Data Science
Course duration	2 years
Eligibility	50% marks in bachelor with a compulsory subject such as Statics, maths, computer
Admission process	Entrance exam based / Merit-based + personal interview + counselling
Course fee	INR 2 lakhs to INR 4 lakhs
Salary	INR 4 lakhs to INR 8 lakhs per year
Career options	Scientist, field officer, manager, research assistant, lab counselling professor, researcher and accountant, and statistician.
Employee role	Data Architect, Vice President, Business Operations Manager, Technical Product and Program Manager, Data Science Manager, Leading Manager, Analytics Manager, Business Intellig, CEO

Eligibility for master’s in data science

The data science course eligibility criteria is as follows –

• Eligibility for the M.Sc. in Data Science

The data science course eligibility is for those who meet the requirements. Students must have completed a bachelor’s degree in mathematics, statistics, or computer science from an accredited institution with a minimum cumulative GPA of 50%. They must study statistics or mathematics for at least two years.

• MBA in Data Science eligibility criteria

The first step in determining your ability to apply for an MBA programme is to determine your MBA eligibility. A graduation grade point average of at least 50% is required for general MBA eligibility. Universities such as Manipal Academy of Higher Education (MAHE) require at least one year of experience to apply for an online MBA with Data Science as a specialisation.

Programme fee

The data science course fee for master’s in Data Science varies between colleges. The data science course duration and fee are also influenced by the type of schooling. M.Sc. in Data Science courses typically cost between INR 1 lakh and INR 4 lakhs in total along with the duration of two years. Each college has different tuition since it relies on the amenities it offers.

Top data science institutions 

Here are the top institutions that offer data science courses – 

• Manipal Academy of Higher Education (MAHE)
• Chandigarh University 
• VIT Vellore
• Christ University, Bangalore
• Sastra University, Thanjavur

Masters in the data science course syllabus 

Data science course syllabus and curriculum address these three fundamental subjects in depth: calculus, descriptive statistics, and programming. Comprehensive instruction is provided in the latest technologies, including ML, DL, Python, and Spark.

The following table displays the M.Sc. Data Science course syllabus and data science course subjects per semester – 

Data science course syllabus

Semester I Mathematics for Spatial SciencesApplied StatisticsFundamentals of Data SciencePython ProgrammingIntroduction to Geospatial TechnologyProgramming for Spatial SciencesBusiness CommunicationCyber SecurityIntegrated Disaster Management	Semester II Spatial Big Data and Storage AnalyticsData Mining and AlgorithmsMachine learningAdvanced Python Programming for Spatial Analytics Image AnalyticsSpatial Data Base ManagementFlexi-Credit Course
Semester III Spatial ModellingSummer ProjectWeb Analytics    Artificial Intelligence    Flexi-Credit CoursePredictive Analytics and Development	Semester IV Industry ProjectResearch Work

Key skills you will gain from a master’s programme in data science 

Following are some of the significant hard and soft skills that you may build from a relevant data science courses – 

Hard skills 

Data science is a rapidly developing field. Technical expertise at a high level can help you rise more quickly by enabling you to develop a special skill set.

• Machine learning/artificial intelligence

A developing, potentially unending future of computer evolution is made possible by the development of neural networks that enable decision-making, picture and speech recognition, problem-solving, and translation in computers.

To forecast future business results and automate customer care processes, AI is also employed in data analytics.

• Data analysis 

Data science cannot exist without data analytics. Because data science is fundamentally sophisticated analytics, there is an overlap between the  work of data scientists and analysts. Calculating numbers on a large scale is data analytics.

• Software development

With methods like regression, optimisation, clustering, decision trees, random forests, and predictive models powered by powerful software, data science is a highly specialised area of statistical research. Data scientists employ operating systems and programmes made by software engineers to gather and analyse data.

Soft skills

More than just technical expertise is necessary for a career to develop and prosper. To negotiate the office hierarchy and team-oriented structure present in the majority of firms.

• Communication

Working with coworkers and presenting the results of data analysis are two additional aspects of communication that go beyond speaking and writing effectively in data science. 

• Critically analysing

Critical thinkers view the universe as a puzzle that needs to be solved. They question established beliefs and look into evidence to the contrary. Employers want workers that never stop learning and have a broad variety of analytical skills.

• Ethics

Data science ethics involve more than simply acting morally. A fantastic place to start with computer ethics is to realise that data, even in its most basic form, can be interpreted and turned into a story. 

Jobs roles and salary in data science 

Job roles	Average annual salary
Data Analyst	INR 4.3 LPA
Data Engineer	INR 8.2 LPA
Data Architect	INR 23.1 LPA
Machine Learning Engineer	INR 7.5 LPA
Data Scientist	INR 10.6 LPA
Statistical Programmer	INR 6.5 LPA
Data Developer	INR 5.3 LPA
Data Science Teacher	INR 2.3 LPA
Data Mining Engineer	INR 2.1 LPA
Database Administrator	INR 10.1 LPA

Career progression in data science 

After three to seven years of experience with data, senior data scientists may be promoted. While mid-level data scientists construct the statistical models that will provide answers to problems, senior data scientists use that model in conjunction with other cutting-edge methodologies.

Top recruiters

These are the four biggest data science recruiters in India – 

• Amazon
• Deloitte 
• Flipkart 
• Accenture 

Benefits of pursuing master’s in data science

Data science is a fantastic field to work in. Both the demand and the salaries are high. With a  master’s degree you can expand your career prospects.

• Gain proficiency in data management technologies

One clear benefit of obtaining an advanced degree in data science is that you will have the chance to become knowledgeable in data management technologies.

• Earn a salary that’s more impressive

Like almost all careers, individuals who have masters in data science salary will be significantly influenced by their level of schooling. A data scientist with the same amount of work experience and a master’s degree will likely make more money annually than someone with only a bachelor’s degree.

• Gain credibility

A master’s degree will provide the student who successfully completes the programme credibility, especially one that calls for completion and defence of a capstone data science project.

• Enter a field that is exciting and future-focused

There is no denying that the study of data science is immensely fascinating and exciting. You have the opportunity to go more deeply into data science during your master’s degree programme than you did during your undergraduate studies. 

Will pursuing an online master’s degree in data science help me?

An online master’s degree can be an effective instrument for career advancement. Understanding the numerous advantages of completing your master’s degree online is crucial in light of this.

• Growth

You can further specialise on more complex topics like machine learning and artificial intelligence based on your skill set and interests. These are some of the most promising employment paths, and MAHE M.Sc. in Data Science will make you stand out from the crowd.

• Personal aptitude

Knowing your personality traits might help you decide whether a job in data science is right for you. 

• Personalised Instruction

You have complete control over your learning process when you complete your coursework online. This enables each student to customise their educational experience to suit their unique tastes. 

• Lucrative career

Every organisation and industry makes use of data. There is a rising need for experts in data science who can analyse data, recognise trends, and make deductions. For working professionals, obtaining an MBA with data science would also pave the way for a long career.

Is Online Manipal a good place for a data science master’s degree?

Manipal Academy of Higher Education (MAHE), the top university in India, now provides the best data science courses online M.Sc. and MBA Data Science via the Online Manipal platform. The MBA programme is intended for working professionals who want to advance in their careers. 

The curriculum aims to educate you for analytical and leadership roles in a variety of sectors by fusing big data analytics, machine learning, and statistics in the ideal way, acquiring effective teamwork techniques, tactical and strategic recommendation development, and process, work, and people management.

Closing

Students who want to pursue an master’s in data science should rigorously study the required material well in advance of the exam. This will assist students in adjusting their schedules in order to prepare for the admission tests. Enrol yourself with Online Manipal to get the best masters programme in data science available online. 

Meta Description: Data science courses have all the topics and a framework that will help students develop the abilities needed to succeed as data scientists or in careers related to data science.