There is no doubt about the fact that analytics are the way of the future, but they are also the way of the present.
After being adopted by a wide range of industries, analytics is now used for everything from manufacturing plant maintenance analysis to airline route planning.
With the applications of data analytics booming to paramount proportions it is not just valuable to learn skills in data analysis, it is now a necessity.
This is indeed an interesting time to start a career in data analytics.
Someone who aspires to become a data analyst can take the traditional path. Although the notion seems to have changed over the past few years.
With the arrival of skilling institutes such as Masai, it is now possible to become a data analyst within a space of 30 weeks as compared to 3-4 years by taking the traditional path.
How do we do this? The answer lies within our origins and the way our curriculum has been structured.
The Genesis of Data Analytics at Masai
Masai initially started by educating students in the field of web development. Students came into our curriculum with the expectations of learning the ins and outs of programming languages and we made sure we over-delivered.
Our holistic approach towards teaching and learning made sure students apart from getting better at web development also enhanced their soft skills. This has allowed our students to be successful and elevated a lot of families from below the poverty line.
With 2000+ hiring partners at Masai, we knew that we had the curriculum, pedagogy, and all the infrastructure in place to train our students to be successful in kickstarting their careers.
Given the expertise, we had in running web development courses we decided to launch data analytics courses in association with PrepLeaf which was already a master in their craft.
This is how Masai’s part-time data analytics course saw the light of day- an intensive and immersive curriculum that runs for 30 weeks.
Masai’s Part-Time Data Analytics Course
The syllabus for this course is distributed over 6 units. Each unit runs for 5 weeks.
Anyone in the age bracket of 18-28 years, irrespective of their academic backgrounds working, studying, or looking out for a job can apply to study in the course.
Following is the unit-wise breakdown of the part-time data analytics course:
Unit 1 (Week 1-5): Excel & Basics of SQL
From one-person companies to Fortune 500 companies - almost all of them use Excel, and with that, it becomes very important for aspiring data analysts to learn Excel.
It helps aspiring data analysts get acclimatised to data analytics activities and build up a perfect fundamental base to help them make an easy shift towards other tools such as Python and R.
Once Excel is over and done with, the course then moves towards teaching SQL.
SQL brings in the concept of relational databases which opens up the playground for the students to play with a larger amount of data.
This serves as an upgrade from Excel where the data available is relatively smaller. Also, SQL provides an introduction to the students to visualisation of the next level.
Throughout the first unit, students get ample exposure to the curriculum and make a decision on whether or not the course would help them achieve the goal they initially set out to.
Unit 2 (Week 6-10): Python
Python is a dynamic language. It is simple to learn and read.
Python follows developers and data scientists go—and startups hire a lot of both. Startups hire a lot of candidates who are proficient in Python owing to the scalability of the language. Both fintech startups and conventional financial institutions use Python as a component of their technology stack.
Python is used by numerous large corporations, including Intel, IBM, NASA, Pixar, Netflix, Facebook, JP Morgan Chase, Spotify, and many others. Python is one of Google's four primary programming languages, and it's used extensively on YouTube. The same is true of Instagram, Pinterest, and Reddit.
What Python brings to the table is an easy-to-use interface that enables data analysts to establish relevant algorithms in databases. With this unique ability, Python cuts short the time data analysts would otherwise require to model data and further use it for their analysis.
In the longer run Python helps students excel at data analysis.
Unit 3 (Week 11-15): Guesstimate & SQL (Advanced)
The third unit is where students step into studying complex subjects.
Have you ever tried to find out the answers to the following questions:
How many cups of coffee are consumed daily in Delhi?
How many soaps are sold daily in India?
With guesstimate which is a mashup of "guess" and "estimate”, these questions can only be roughly answered.
Guesstimate questions provide insight into the approach you take up in order to respond to a particular question. The ability to think quantitatively, think numerically, and use judgement to arrive at reasonable solutions are tested through guesstimate questions. This strategy demonstrates to a potential employer whether candidates are qualified for the position they are applying for.
Unit 3 gives the students a first-hand experience of solving such questions which are generally asked to start any data analysis interview.
After Guesstimate is done and over with, it is time for the students to build up on the basic SQL knowledge gained during Unit 1.
The latter part of Unit 3 is where a lot of emphasis is placed on teaching Common Table Expressions (CTEs), recursive CTEs, joins, unions, table functions, etc.
Unit 4 (Week 16-20): Case Study, Tableau, Probability & Statistics
During the first three units, students become familiar with basic and advanced tools. Unit 4 is where they get to apply it by carrying out case studies in their designated groups.
Once this hard part is done and over with, they are required to present their findings to their peers and instructors.
In addition to this students also go through lessons on Probability and Statistics.
This is where students learn to make predictions and search for structures in unorganised data. Learning probability and statistics helps them to develop the ability to handle different analytical tasks.
Unit 5 (Week 21-25): Machine Learning
As the students enter the closing phases of the course they are introduced to Machine Learning.
Without our knowledge, machine learning has woven itself into the fabric of our daily lives.
Some applications of companies using Machine Learning are-
- Netflix: Netflix uses machine learning to analyse the viewing patterns of its millions of subscribers in a process known as collaborative filtering to make predictions about which media viewers may also find enjoyable.
- Hubspot: HubSpot, a provider of business software for marketing, sales, and services, employs machine learning in a variety of applications. In order to assign predictive lead scores for sales teams to use when determining which customers are prepared to buy their products, it provides content marketers with insight into what search engineers associate their content with.
- Microsoft: The Wild Me project is utilising Microsoft Azure, a cloud platform with over 200 services, and its machine learning and DevOps features to combat animal extinction.
Traditional data analytics rely a lot on analysing samples that have been frozen in time. Enough, this might lead to faulty and incorrect conclusions.
Machine learning, suggests clever alternatives to analysing enormous volumes of data by automating the creation of analytical models in real time.
It enables computers to discover hidden insights without being explicitly programmed where to look by using algorithms that repetitively learn from data.
This helps the data analysts a great deal by creating quick and effective algorithms that produce accurate results and analysis thus cutting down the time for analysis.
Knowledge of such an important tool is thus necessary for the students to gain an edge over others when they go out for interviews.
Unit 6 (Week 26-30): Placement preparation & Revision
This is where the students start preparing for the interviews.
By the time students get done with Unit 5 of the Masai curriculum, they complete 5 group projects and accumulate an experience of more than 1000 hours of data analysis.
This is more than what an average engineering student covers in 4 years.
Unit 6 is where students engage in mocks - Test their skills, practice mock interviews, and prepare for the final test of their skills i.e Placements.
Condensing years of education within just 30 weeks is a feat that we have only been able to pull off due to the presence of our highly qualified and experienced instructors.
These instructors bring years of experience to the table which ultimately helps them train students with cutting-edge skills so that they deliver on the job from day one.
One of our instructors who deserve a special mention is Aman Vats. Aman has been a part of our curriculum ever since Masai’s inception and has been able to establish himself as one of the important pillars of our curriculum.
With his exemplary teaching style, he has been able to inspire hundreds of students to forge beautiful careers and lead the curriculum team towards unprecedented success.
Some other instructors that make up our curriculum team are Saurabh Suman, Divya Pratap Singh, Drishti Mamtani, Jyoti Gupta, and Akhilesh Bussa.
Over the past 3 years, these instructors have been able to train and build the careers of hundreds of students who now work in well-renowned startups and tech giants.
Here is a success story that deserves special mention.
Coming from Ghaziabad, a small town in the Indian state of Uttar Pradesh, Marmika Singh enjoys working with numbers and has always been interested in mathematics. She made the decision to pursue a bachelor's degree in mathematics at Delhi University's Kamla Nehru College after high school graduation.
In her own words, Marmika Singh credits Masai’s curriculum for being carefully crafted in a way that would allow her ample time to fully grasp each and every topic covered.
She also spoke highly of the instructors and IAs here who also helped her clear up any questions or doubts in addition to the soft skills classes that helped her improve her communication skills and ultimately land the coveted business analyst job at Uber.
Marmika is just one of those many students who went on to completely change their careers and become an inspiration for other aspiring data analysts out there.
It is because of extraordinary success stories like these that we constantly see an influx of students to our skilling institute in huge numbers.
What are the possible reasons that are bringing about this change? Why do thousands trust us to help them get to their dream tech job?
Let us break it down for you.
This is a comprehensive approach where Masai focuses on the development of the intellectual, mental, and social abilities of an individual in order to ensure that they inculcate within them the qualities necessary for facing the demands and challenges of the professional field.
Students stay in our curriculum only for 30 weeks. Thus, it is necessary that when they bid us adieu they are well off on their own to execute their job responsibilities down to the tee. This is where mastery-based progression comes into play. With mastery-based progression (MBP), the focus of the learning process moves from surviving to learning. MBP ensures student proficiency and quality of education by preventing a student from moving forward until they have mastered a particular subject. This framework strengthens the core competencies that make up a software developer or a data analyst.
Pay After Placement (P-A-P)
In a pay-after-placement model agreement, a person or organisation provides training or a loan to a recipient in exchange for that person or organisation sharing a specific percentage of their income for a specified period of time or until the agreed-upon loan has been repaid.
PAP takes a lot of pressure that arises due to their financial situations off of the students’ shoulders. It eases the path for the students to gain high-quality education without any financial worries. It eliminates the fear of the debt that starts lurking on the top of their head once they graduate.
Taking into consideration India’s age demographic and the potential that lies within, Masai’s PAP program has proven instrumental in helping India’s youth realise and launch their professional careers.
Many students in our country stay deprived of higher education owing to financial constraints.
A handful of those who actually attain higher education go as far as building a great career.
There is a large chunk of the young population whose potential still remains untapped. This is an alarming situation that has been born out of individuals either not having access to resources or not getting the support they need.
The P-A-P model takes care of the resource problem and the burden of the student’s success gets shared. The shared responsibility drives us even more to take every step that would ensure their success.
The sense of inclusivity allows us to take any kind of help the student needs through their tenure and eventually get them where they aspired to be.