The Matchless Master’s Program in Data Science and Machine Learning
We are constantly witnessing the impact of the internet and the world and how the latest technologies like Artificial Intelligence, Machine learning, Data Science, and many more are advancing day by day. With the advent of the internet in the 20th Century and the increasing rate of usage of technology across the globe has driven the availability of massive amounts of data. The use of computers and smartphones has led to the necessity to process, store, analyze the data to make effective use of the same in the future. To appropriately store and make use of available data, we need Data Science and Machine learning.
Data Science and Machine Learning are the two latest technologies we hear people talking about. These two technologies have gained massive demand in recent times. Let us look into these two terms and learn about them. We are living in a world that generates huge amounts of data on a daily basis. Dealing with data is not an easy task as massive amounts of data are being generated every minute around the world. The domain of Data Science concentrates on working with large amounts of data.
Let us learn what Data Science exactly is…
What is Data Science?
In simple words, Data Science can be defined as the set of tools and techniques applied for obtaining information and insights out of any given data. These insights are applied to solve real-world problems efficiently.
Data Science involves several steps to derive the desired insights such as Data Extraction, Data Analysis, Data Visualization, Data Management, and Data Storing. All these steps are carried out to convert raw data into useful information. The application of these steps helps to derive impactful insights that help in making effective decisions.
What are the various job roles of a Data Science Professional performed on a daily basis?
Many people get mixed up about the duties of data scientists. There are also assumptions that data scientists perform jobs like Data visualization, data processing, data munging, data mining, etc. However, let us comprehend what the job of Data scientists looks like and how the role of Data scientist impacts an organization. Data scientists are the backbone of any organization today as they perform many key roles that impact the growth and development of the organization.
- Data Science plays a vital role in understanding business requirements and solving business problems by accessing the given set of data.
- Data collection is a tiresome and significant part of a data scientist. This process involves retrieving historical data that is necessary to perform pivotal operations.
- The next step involves cleaning the data. This is another important responsibility to be taken up by Data scientists which involves the assessment of the collected data and removing desirable components of the data. This task reduces complexity and makes it easy to arrive at the right solution.
- Post cleaning the data, Data scientists perform data exploration and data analysis. This is an integral action performed by a data scientist. Data exploration is tantamount to a brainstorming session data analysis as this involves the application of several techniques to the given set of data in order to derive meaningful insights. Getting acquainted with the patterns of data helps you derive the most accurate results to solve specific business problems.
- Data modeling is the next phase where a data scientist performs the application of several machine learning algorithms to the given data and detecting the patterns and after deriving necessary insights of the data. The data modeling phase gives the most accurate predictions and the best solutions to define any given problem.
- The next phase is Data validation. In this phase, the selected model is tested to notice if there exist any inconsistencies. This phase is crucial as this helps to recognize false predictions, errors, and undesirable insights retrieved in the above stages.
- After performing all the above-mentioned operations, the data scientist is now aware of the efficiency of the selected model and he gets ready to deploy the results acquired.
- After the deployment, the data scientists receive feedback and make necessary corrections considering the comment received.
Payscale of Data Scientists
The domain of Data Science is recognized as one of the leading career professions in India for the immense demand it has attained in recent times. The job roles in the domain of Data science are observed to offer one of the highest salaries. Hence many youngsters are desiring to pursue their masters in data science. The salary scale in this domain varies from 4 lakh per annum to 25 lakh per annum considering various factors. In India, the average pay scale of a data scientist is observed to be 7 lakh per annum. Now that we have learned about the overview of Data Science let us look into Machine Learning
What is Machine Learning?
Machine Learning is a subset of Artificial Intelligence. Machine Learning is the science of teaching computers to learn and act like humans by intaking data without being explicitly instructed.
What are the various job roles of a Machine Learning professional performed on a daily basis?
- Study and Transform Data Science Prototypes
- Design an effective machine learning system
- Analyse and implement the most appropriate Machine learning algorithms
- Build effective Machine learning applications considering the organizational requirements.
- Choose the most appropriate data set and data representation method to apply.
- Implement statistical analysis
Payscale of Machine Learning Professionals
The job roles in the domain of Machine Learning are believed to be one of the highest paid jobs in the country. The pay scale of the people working in this domain varies from 5 lakh to 30 lakh in India. However, the average pay scale of a Machine Learning professional in India is estimated at 7.5 lakh per annum. The various job roles in the domain of Data Science and Machine Learning have now evolved as the most in-demand career professions of the 21st century. Many youngsters are aspiring to pursue a career in these domains and are seeking to pursue
The Exciting combination of Data Science and Machine Learning
Today's world needs a lot of Data Science and Machine Learning professionals. There is an exponential growth in the demand for job roles in the domains of Data Science and Machine Learning. Considering this demand, a few Universities across the world are offering advanced programs in Machine Learning and Data Science. Since Data Science and Machine Learning are the two domains that are intertwined to each other, choosing a master's in Data Science and Machine Learning is highly preferable than aiming for an MS in Data Science or Machine Learning Master Degree.
In a broader sense, Machine learning is a subset of Data Science. However, there is a world of difference between the roles associated with these two fields. The responsibilities of a Data scientist vary in an enormous manner from the responsibilities of a Machine learning engineer. There is a prevalent confusion among the professionals concerning the difference between these two roles. We shall discuss such differences in this article and shed some light on the demand for the combination of Data Science with Machine learning.
The hard job of being a machine learning engineer is to pick the right algorithm that suits the requirement. It is a tremendous effort to come up with such a solution. This is the area where Data Science will play a vital role. Data scientists do a statistical analysis to find which algorithm suits the best for a particular problem. Machine learning engineers take the prototype model that Data scientists came up with and implement the same. Machine learning engineers feed the data into the models provided by the data scientists. Along with that, they are liable for considering theoretical models and helping data scientists to scale the models to production level models.
Data scientists would analyze the data provided by an organization and design models using programming models to reinforce analytics. They will also make use of online experiments besides other methods to aid businesses in achieving sustainable growth. Additionally, Data scientists can develop personalized data products to help companies better understand the aspects of their company and their customers to make better business decisions.
The following example befits the above explanation
Data scientists translate the problem into a technical model. Say, a company needs to know which product to be sold the most in the next few years considering the technological patterns and demand for the product in the market. Data scientists would analyze the data and build a model. Machine learning engineers would take that model and apply the same across the organization.
A combination of Data Science and Machine Learning would transform professionals into exemplary craftsmen. From the discussion, it is apparent that organizations need to rely on both Data scientists and Machine learning engineers to boost their profit. A single individual with both of these skills would be high in demand as they would cut down the cost for two individuals who are experts in each area. Often, professions struggle to grasp the work performed by peers prior to them thereby causing significant investment to comprehend their work and come up with the solution to move forward. The area of Data Science and Machine Learning is no exemption to that. It gives the flexibility to the professional to make necessary changes to the model upon the dysfunctionality of the model.
In a realistic sense, the combination of these tools is not limited to IT alone. Data Science and Machine Learning are being applied in a wide range of industries such as E-commerce, Gaming, Healthcare, Financial sector, Transportation, Customer support, Social media and many more.
What are the Prerequisites to pursue a master's in Data Science and Machine Learning?
Below are the various prerequisites to master the concepts of Data Science and Machine Learning
1. Mathematics : Being good at mathematical and statistical concepts such as probability distributions, hypothesis testing, statistical significance, and regression would help gain a better understanding of the various concepts of Data Science and Machine Learning. Learning the key concepts of probability such as conditional probability, priors, and posteriors, and maximum likelihood makes it easy to understand the concepts of Machine Learning. Therefore, mathematics and statistics are considered as one of the major prerequisites to master these two technologies.
2. Programming Languages : Getting acquainted with the knowledge of various programming languages would help you master Data Science and Machine Learning. Java, C++, R, Python, etc are a few languages that are broadly used in Data Science and Machine Learning. Python is recognized as the highly preferred programming language by many Data Science and Machine Learning professionals because of the availability of libraries in Python.
3. Database Concepts : Being familiar with several database concepts and tools such as big data, SQL, SAS, etc would offer to gain a comprehensive understanding of the theories of Data Science and Machine Learning.
4. Data Visualization Tools : Having a brief knowledge of data visualization tools such as Tableau, Datawrapper, FusionCharts, etc would help effective presentation.
Non-Technical Prerequisites :
There are also a few of the non-technical prerequisites to become a successful Data Science and Machine Learning professional
1. Strong verbal and visual communication skills : Working as a Data Science and Machine Learning professional, you will need to interact with a lot of people on a daily basis. Possessing strong verbal and visual communication skills helps you to effectively communicate and convince people to promote better understanding.
2. Ability to work with a team : As you will be working with a lot of teams, it is very important to possess the spirit of teamwork to get along with all the teams well.
3. Problem-solving skills : Problem solving skills are extremely necessary for someone working in these domains. Understanding a specific problem and discovering the most effective and efficient way to address it is one of the major job roles of a Data Science and Machine Learning professional.
What are the various job roles offered for the candidates pursuing a Masters in Data Science and Machine Learning?
The domains of Data Science and Machine Learning offers a wide range of exciting career opportunities.These job roles are one of the major reasons that are driving interests in youngsters to take up a data scientist masters program or a master in machine learning online.
Let us know a few of them.
1. Data Analyst
2. Operations Analyst
3. Data Engineer
4. Data analytics consultant
5. Database administrator
6. Quantitative Analyst
7. Data Scientists
8. Data Architect
9. Machine Learning Specialist
10. Business Analyst
11. Machine Learning Engineer
12. Data Mining Specialists
The comprehensive curriculum of the master's program in Data Science and Machine Learning by Great Learning
The master's program in Data Science and Machine Learning offered by Great Learning in collaboration with Reva University comprises the full set of Data Science and Machine Learning courses you need to develop and equip yourself with the complete skill set desired to fit into the Data Science and Machine Learning job roles. The curriculum of this program is in line with industry standards and it consists of two semesters in the first year followed by the Master’s thesis and a capstone project spread over 21 months.
The first semester of this program mainly focuses on laying a strong foundation on the various prerequisites needed for learning Data Science and Machine Learning. This module lasts for 6 months.
Python for Data Science
Python, being one of the most popular programming languages, has gained immense demand. Python is strongly needed to learn Data Science and Machine Learning. Though there are a lot of other programming languages like R, Java, C++, and many more, Python is highly preferable for the outstanding flexibility it offers in performing various statistical and analytical operations by Data Scientists and Machine Learning professionals.
Below is the list of the various python related concepts covered in this module.
1. Python basics, data structures & data handling
Statistical Foundations for Data Science
Statistics is one of the most important aspects of Machine Learning and Data Science. Statistics is also a major prerequisite for mastering Data Science and Machine Learning. Hence, in the first module, the students will be taught all concepts of statistics that are mandatory to empower them to gain a strong understanding of the various concepts of Data Science and Machine Learning throughout the course.
Below is the list of the various statistical concepts covered in this module.
1. Statistics (Descriptive, Inferential)
3. Hypothesis Testing, ANOVA
4. Causality and ‘Fit’
5. Measures of Dispersion
6. Regression lines and error terms
Databases - SQL & NoSQL
As Data Scientists and Machine Learning professionals ought to work with a huge amount of data on a daily basis, it is necessary to be familiar with database concepts and tools like SQL. So, the students will be taught the various concepts and applications of Databases.
Below is the list of the various database concepts covered in this module.
1. Database concepts
2. Data Models
4. Comparison with NoSQL data stores
5. Common NoSQL tools like Cassandra & MongoDB
Mathematical Foundation for Data Science
Since Mathematics is one of the major prerequisites to master Data Science, Various mathematical concepts will be taught to the students to lay a strong mathematical foundation to learn the concepts of Data Science and Machine Learning.
Machine Learning - I
In this module, the candidates will learn about Machine Learning and the various stages involved in the Machine Learning workflow and the deployment.
Data Visualization using Tableau
Data Visualisation can be defined as the graphical or pictorial representation of data by using several visualization elements like charts, graphs, maps, and many more accessing data visualization tools. In this module you will also learn about various data visualization tools and techniques applied by Data Science and Machine Learning professionals.
1. Visualization principles
2. Exploratory Data Analysis (EDA)
3. Tableau for Visualization
4. Python packages for visualization
5. Presenting insights
Structuring & Vizualizing Analytics Problems (SVAP)
In this module, the students will develop knowledge of structuring and visualizing Analytical Problems.
As you get familiar with the core concepts of Data Science and Machine Learning, you will be learning several Data Science and data analytics techniques and skills. The second semester mainly focuses on teaching the in-depth concepts of Data Science and Machine Learning.
Machine Learning – II
The second module of Machine Learning teaches ensemble and classification.
1. Supervised Learning - Classification (Logistic Regression, kNN, Naïve Bayes, Support Vector Machines)
2. Feature Selection
3. Ensemble Techniques in Machine Learning - Decision Trees, Random Forests, Bagging, Boosting
Natural Language Processing
Natural Language Processing is an integral part of Machine Learning that deals with developing the ability of a computer to understand, and analyse a human language. In this module, you will learn the various tools and techniques involved in Natural Language Processing.
Machine Learning – III
The third module of Machine Learning teaches unsupervised learning and Recommendation systems.
- Unsupervised Learning - Clustering (k-means, hierarchical, etc.), PCA
- Ensemble Techniques in Machine Learning - Decision Trees, Random Forests, Bagging, Boosting
- Features of a Cluster - Labels, Centroids, Inertia
- Eigen vectors and Eigen values
- Principal component analysis
Time Series Forecasting
Time Series Forecasting is another major aspect of Data Science. The process of Time Series Forecasting can be defined as the set of tools and methods applied for analyzing the data recorded in different time series to derive meaningful insight.
Once you gain a clear understanding of the Machine Learning concepts, you will now enter the third module which lets you learn about Deep Learning. Deep Learning is again a subset of Machine Learning.
Intro to Deep Learning and its applications
1. Neural networks
2. Deep Neural Networks
3. CNN's and their application to Computer Vision
4. RNNs/LSTMs and their application to Natural Language Processing
Intro to Big Data Analytics : The tools of Big data are essential for Data Scientists to manage and analyze massive datasets. Hence, you will learn the various tools and techniques of Big data.
1. Intro to Hadoop
2. Spark & ecosystem of tools
5. Batch processing (Hive, HBase, ingestion)
6. Real-time processing (Kafka, Spark Streaming)
7. Deploying ML code using ML pipelines on the cloud
Business Analysis and Communication : Excellent communication skills is one of the most important skills any Business Analyst should possess. This module concentrates on training the students to develop effective business communication skills .
Year 2 :
The second year of this program completely focuses on accelerating Hands on learning followed by various career assistance programs.
Capstone Project : Once the candidates gain a comprehensive knowledge of the above-described concepts, they will be assigned a capstone project that necessitates the application of all the skills and knowledge gained throughout the classroom sessions. The capstone project is designed to help the candidates gain expertise in working with various tools and techniques learned in the classroom. Working on the capstone project also helps the candidates gain complete confidence to become a successful Data Science and Machine Learning professional.
Master Thesis : The students will be constantly guided in writing a well structured Master's thesis that displays all the knowledge and skills acquired throughout this program. The thesis also includes all the research findings obtained in the Capstone project.
Career Assistance : After finishing the capstone projects, candidates will also be engaged in various career assistance programs.
Below are the various Career assistance programs organized by Great Learning in association with Reva Institute
1. Resume building
A well-built resume would surely help to market yourself the best way. Building an interesting resume demonstrates your skills and expertise with the help of experts.
2. Mock interviews
Great Learning organizes regular workshops that help you prepare for technical interviews conducted by industry experts in Data Science and Machine Learning. Participating in these workshops help you enhance your interview skills.
You will be provided with a dedicated program manager who solves your queries associated with the program and helps you attain a comprehensive understanding of all the concepts of the program.
4. Career fair
Take part in the regularly organized job fairs by Great Learning across the various cities.
Earn a Master's Degree from REVA University
Upon the successful completion of this program, candidates will earn a Master's degree in Data Science and Machine Learning from the prestigious Reva institute. Reva is one of the highly reputed private institutes of the country, established in the year 2002. In no time, Reva has gained popularity and appreciation for the top quality education it offers to its students.
Reva is recognized as India's best Private university by India today. Reva has been ranked 3rd best university in Karnataka by KSURF. In fact, Reva is also awarded as Asia's most trusted brand.
Why Choose Great Learning?
There is an exponential rise in the number of institutes organizing classes machine learning and data scientist masters program. It is essential to choose an institute that offers the best Data Science masters program. But, why is it so essential to choose the best one?
Well, the institute that you choose plays a mandatory role in shaping your career. So, you should make a wise decision in choosing the right institute.
Among many institutes offering Machine Learning and Data Science Master Degree courses, Great Learning has been observed to offer outstanding programs in terms of curriculum and faculty.
Here are a few traits that make Great Learning the best choice to pursue a Masters in Machine Learning and Data Science Online.
The faculty crew of Great Learning is highly esteemed and also recognized as one of the best in the country. This program is being offered in collaboration with the renowned Stanford University. So, when you take up the Data Science and Machine Learning course by Great Learning in Mumbai, you will also be taught by the famous Stanford faculty members.
The masters program in Machine Learning and Data Science online by Great Learning intensely employs hands-on learning. Candidates will be working on multiple lab sessions to practice the tools and techniques they have learned during the classroom sessions. Working on various real-world problems aids the candidates in gaining hands-on expertise and equips you with the skills required to start up a career in Data Science and Machine Learning.
Each of the candidates of this program will be allocated a mentor who is a real-world practitioner of Data Science and Machine Learning. The mentor constantly guides the candidate in each step of their learning journey. Possessing deep knowledge on Data Science and Machine Learning, the mentors also play a major role in aiding the candidates to gain an in-depth understanding of the concepts as they share valuable insights out of their practical experiences.
Great Learning is one of the best institutes that offer a Machine Learning and Data Science master's degree online program. As Great Learning is associated with top-notch organizations across the country, each of the candidates will be provided placement assistance. Great Learning assists 350+ reputed organizations with 30 - 40 placements every month. Candidates will be participating in regular placement drives organized by Great Learning. The list of the organizations includes Amazon, Oracle, HCL, Accenture, Cognizant, IBM, JPMorgan, Wipro, EY, Microsoft, Infosys, Swiggy, Citibank, and a lot more.
As the classes take place during the weekends, this course offers you the immense flexibility to earn while you earn. If you are seeking to pursue a machine learning and data science online degree program, make the best use of this opportunity.
Design your career the best way possible by registering yourself with the best Data Science and Machine Learning masters program today. For further details, do get in touch with our team today and grab your seat at the earliest
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