Data Science Course Duration: Become Data Scientist Quickly!

Data science is the study of obtaining valuable information for business decision-making, strategic planning, and other purposes by utilizing cutting-edge analytics tools and scientific concepts. Data science drives organizations into better marketing and sales campaigns and boost operational effectiveness.

Data science is a field that is always evolving. It uses computer science and statistical techniques to gather information and produce insightful forecasts across various sectors. The term “data science” and the discipline have evolved. Recent years have seen a huge increase in popularity due to developments in data gathering, technology, and the mass output of data internationally. 

According to Coursera, Data scientist jobs are predicted to rise by 36% between 2021 and 2031. If you are looking to become a data scientist or switch your career to data science, here are some of the top courses you can pursue. 

Read more: Is data science a good career option for you?

Courses in data science

When pursuing a degree in data science, you will be intrigued by the variety of courses you have to choose from. Below is a list of the different categories of data science courses.

MSc in Data Science

An MSc in Data Science is a postgraduate academic degree program that focuses on providing students with advanced knowledge and skills in the field of data science. The program typically covers topics such as statistics, machine learning, data mining, big data analytics, data visualization, and programming languages commonly used in data analysis such as Python and R. 

Read more: How to grow in your career with a master’s in data science

Postgraduate certificate in Data Science

The duration of a Postgraduate Certificate in Data Science varies but is typically shorter than a full master’s degree program, ranging from a few months to a year. These programs are often suitable for individuals who want to acquire specific skills in data science or want to upskill in data science. There are plenty of online data science certifications available, making it easy for working professionals to upskill without quitting their jobs.

MBA in Data Science

An MBA in Data Science is a specialized Master of Business Administration (MBA) program that focuses on integrating business management principles with advanced skills and techniques in data science. This program is designed to prepare students for leadership roles in organizations where data-driven decision-making is essential for success.

In an MBA in Data Science program, students typically learn about topics such as data analysis, machine learning, predictive modeling, data visualization, big data analytics, and statistical methods. They also study business-related subjects such as strategic management, finance, marketing, operations management, and organizational behavior.

The goal of an MBA in Data Science is to equip students with the knowledge and skills to effectively use data to inform business strategies, improve operational efficiency, drive innovation, and gain a competitive advantage in the marketplace. Graduates of this program are prepared to pursue a variety of career paths, including data science leadership roles, business analytics consulting, entrepreneurship, and executive positions in a wide range of industries.

How long does it take to learn Data Science?

The amount of time needed to master data science principles can be divided into three categories. Depending on your experience and how much time you are ready to devote to your data science studies, the time needed to reach a particular level of proficiency will vary. 

People with analytical backgrounds, such as those in physics, mathematics, science, engineering, accounting, or computer science, typically need less time than people whose backgrounds are not complementary to data science. Let us know more about how much time it takes to learn data science.

Read more: Data science salaries

Basic (6- 12 months)

At level one, a prospective data scientist should be able to work with datasets typically supplied in CSV file format. They should be proficient in data fundamentals, visualization, and linear regression.

The basic-level data science curriculum

• Basics of data: Data manipulation, cleaning, scalability, and engineering skills are required. The following abilities should be present:

1. Understanding the import and export of CSV file-format data.
2. Utilize dimensionality reduction methods, such as principal component analysis, to compress data (PC).
3. Possess the ability to prepare and arrange data for analysis or model construction.
4. Having the ability to scale data utilizing methods like normalization and standardization.
5. Understanding how to handle missing values in a dataset.

• Data visualization: Know the fundamental elements of effective data visualization, the ability to use R’s ggplot2 package, the Python programs matplotlib and seaborn, and other tools for data visualization. should be familiar with the following important elements of effective data visualization:
• Data elements: Knowing the type of data, such as categorical data, discrete data, continuous data, time-series data, etc., is a crucial initial step in selecting how to show data.
• Geometrical element: You must now choose the type of visualization that best represents your data, such as a scatter plot, a line graph, a bar graph, a histogram, a Q-Q plot, a smooth density, a boxplot, a pair plot, a heat map, etc.
• Component of mapping: In this situation, you must choose which variable to use as the x-variable and which variable to use as the y-variable. This is crucial, especially if your dataset has multiple dimensions and features.
• Scale elements: You can choose the scales you want to use here, such as linear scale, log scale, etc.
• Component labels: This covers elements such as axes labels, titles, legends, the appropriate font size to utilize, etc.
• Ethical elements: Make sure your visualization accurately depicts the situation in this case. When cleaning, summarizing, modifying, and making a data visualization, you need to be mindful of your activities to avoid deceiving or manipulating your audience.
• Supervised education (Predicting Continuous Target Variables): Understand linear regression and other sophisticated regression techniques. Possess proficiency in using tools for creating linear regression models, such as caret and scikit-learn. Possess the skills listed below:

1. Understanding how to use NumPy or Pylab to execute simple regression analysis
2. Using Scikit-Learn, be able to do multiple regression analysis.
3. Recognize regularized regression techniques such as Lasso, Ridge, and Elastic Net.
4. Identify other nonparametric regression techniques, such as KNR and SVR (SVR).
5. Determinate numerous measures for assessing a regression model, including R2, MSE (mean square error), and MAE (mean absolute error).

Intermediate (7 – 18 months)

The following competencies should be present in addition to the basic level abilities and competencies:

The intermediate-level data science curriculum

Supervised education (predicting discrete target variables)

Recognize binary classification algorithms like:

• Classify using perceptrons and logistic regression.
• Create models using Scikit-Learn.
• Recognize several metrics, such as accuracy, precision, sensitivity, specificity, recall, f-l score, confusion matrix, and ROC curve, for assessing the quality of a classification system.

Hyperparameter tuning and model evaluation 

• Ability to integrate estimators and transformers in a pipeline.
• Capability to draw and analyze a receiver operating characteristic (ROC) curve.
• Knowledge to evaluate model performance using k-fold cross-validation.
• Understanding of how to read and comprehend a confusion matrix.
• Working knowledge of learning and validation curves for debugging classification algorithms.

Combining different models for ensemble learning

• To employ the ensemble approach with several classifiers.
• Ability to integrate several classification algorithms.
• Understanding how to assess and fine-tune the ensemble classifier.

Advanced (18 – 48 months)

Worked with many datasets, including text, photos, speech, and videos. Should possess the following competencies in addition to basic and intermediate skills:

The advanced-level data science curriculum

• Clustering Algorithm (Unsupervised Learning)
• Cloud Systems (AWS, Azure)
• K-means
• Theano
• Deep Learning

Skills you will learn during a data science program

The pointers mentioned are the prerequisites to learning data science –

• Math and statistics        
• Analytical skills
• Knowledge of programming languages like Python, SQL, and R.     
• Machine learning     
• Soft skills (communication and presentation skills).

Read more on data science career path.

Conclusion

Building a strong online presence, taking the top data science courses available, and adding them to your resume can help you stand out to companies. It will boost your likelihood of getting employed by demonstrating that you have the problem-solving and teamwork skills necessary to be successful as a data scientist. 

Additionally, you can explore the various data science courses available on Online Manipal data science course will help you succeed in your job; if you need assistance deciding which course to choose, get in touch with us. We believe this data science learning course will help you pursue a career in this fascinating and rapidly expanding industry. Enroll now to learn data science online.