Topics You Will Be Learning in
MBA in Data Science in 2022
Synopsis:
The world of data science is ever-changing, and while some techniques will fade away or be replaced, new ones will pop up. Here are the top five techniques you should be using in MBA in Data Science in 2022:
- Industry-specific analytics
- Automated reporting
- Machine learning for data cleansing
- Data lakes and data lakes analytics
- Data discovery
General introduction about MBA in data science
Data science is a field of study that requires understanding the concepts, tools, and techniques used to extract information from data. It touches on many other areas, including computer science, statistics, mathematics, and business.
Data science is about helping people make better decisions by collecting, analysing, and presenting data in the most effective way possible. To do this well, you need to understand how data can be collected and analysed and what questions can be answered by looking at it.
To become a data scientist, you will need to learn to use programming languages such as Python or R and statistical analysis software like SAS or SPSS. You will also have to know about databases like SQL Server or MongoDB, which store large amounts of information in an organised way so it can be accessed quickly whenever needed by different applications running alongside each other without having any conflicts between them running at once.
MBA in data science programs are designed to prepare students for a career in this rapidly-growing field by teaching them how to collect and analyse data and use this information for decision-making purposes. In addition, students will learn about concepts such as machine learning and artificial intelligence, which are increasingly important in today's society.
Top 10 techniques in MBA in data science
This section will provide an overview of some of the most common techniques used in data science...
- Regression
- Classification
- Linear regression
- Jackknife regression
- Logistic regression
- Personalization
- Lift Analysis
- Decision tree analysis
- Binary and multiclass classification
- Anomaly detection
1. Regression
Regression is a technique used to predict the value of a variable based on one or more other variables. It can be used for forecasting, where you want to predict the future value of a variable, and also for modelling, where you want to understand the relationship between variables and their impact on each other.
Regression is one of the most popular tools in data science because it allows us to make predictions about unknown variables using known variables. For example, if we know that the temperature outside is 25 degrees Celsius with a wind speed of 5 km/h and that it's raining, then we can use regression to predict how slippery the road will be this afternoon when people drive home from work.
2. Classification
Another statistical method allows you to classify data into different groups based on their attributes. It's widely used in machine learning because it helps computers learn how to identify patterns without being explicitly programmed for each task. For example, classification can be used to determine whether an email message is spam.
3. Linear regression
Linear regression is one of the most common techniques used in online MBA in data science. It predicts a dependent variable based on one or more independent variables. The main goal of linear regression is to find a line that best fits your data points. This can be done using simple linear regression, multiple regression, stepwise regression, weighted least squares, and ridge regression. These regressions are helpful for both business and academic purposes.
4. Jackknife regression
Jackknife regression is also a common technique used in online MBA in data science because it is relatively easy to implement and understand. It involves removing one observation from your dataset at a time and predicting its value using the rest of your data points (the remaining observations). You then compare these predictions against the actual values for each observation removed from your dataset to estimate how much each observation contributes to your overall prediction accuracy.
This technique can determine whether there are any outliers within your dataset that might negatively affect its overall fit when using an appropriate model selection method such as stepwise selection or weighted least squares/ridge regression methods.
5. Logistic regression
Logistic regression is an essential technique in data science. It is a statistical method that helps you predict whether an event will happen or not. The prediction is based on a set of variables, and the logistic regression model enables you to determine which variables are statistically significant.
6. Personalisation
Personalisation refers to the process of creating content that is customised to meet the needs of your audience. IT can be used in many ways, such as creating personalised product recommendations based on past purchases by customers or providing different versions of websites based on user data.
7. Lift analysis
Lift analysis lets you determine which factors are most influential in your model. This is an excellent way to identify potential areas for improvement or expansion in your data science project. It can also test whether different models are performing equally well.
8. Decision tree analysis
As the name suggests, decision trees come in handy for delivering decisions. They're formed by splitting groups of similar observations into smaller groups until they're entirely homogeneous. For example, imagine that you have a dataset on salespeople's salaries and bonuses based on their sales performance. Decision trees can help you determine what characteristics make someone a stronger or weaker salesperson to target your recruitment efforts better.
9. Binary and multiclass classification
A binary classification is a form of supervised learning that predicts whether an observation belongs to one class or another. It is often used in predicting credit risk, fraud detection, document classification, and other problems where there are only two possible outcomes.
Multiclass classification is similar to binary classification but involves predicting membership in several classes rather than just two. Multiclass classification can be thought of as a way to group observations into one of several categories based on their characteristics. The most common use case for multiclass classification is image recognition, for example, detecting which dog breed is depicted in an image or video frame.
10. Anomaly detection
Anomaly detection is identifying potential errors or unusual data points within a dataset. For example, if an insurance company uses an algorithm to predict whether or not you will file a claim based on your driving history and location data, anomaly detection would flag any instances where its predictions differ significantly from reality.
Why choose Online Manipal for MBA in data science?
Manipal Academy of Higher Education (MAHE) is one of the top universities offering an online MBA in Data Science. It has a track record of providing high-quality education and has been ranked as one of the best schools in Asia by several prestigious publications. Here's why you should choose Online Manipal for MBA data science course:
- Flexibility in curriculum and timing
Students can study at their own pace, which means regular college timetables do not bind them. They can study anywhere and anytime with an internet connection. - Low cost
The fees of an Online Manipal degree are deficient compared to the fees of a regular college. Also, there are no hidden charges like hostel fees, mess fees, etc., which you would have to pay if you join a traditional college. - Better placement opportunities
Manipal Academy of Higher Education (MAHE) has tie-ups with many companies around the globe that help students get placed easily after completing their course at this institute. Students who want to work abroad can also join this course because it is recognized internationally, so it does not matter if you are from India or any other country!
Nutshell
An online degree courses by Online Manipal is the best way to get your degree. It is also affordable and has a flexible schedule. Plus, you don't have to sacrifice quality for the convenience of any topic. The faculty are highly qualified and experienced, and they've designed the course curriculum to be as diverse as possible.