Have you ever considered how Netflix recommends videos of your choice? How does Facebook automatically tag the recognized individual face? Or how do banks identify the loyal customers and which are likely to leave for a competitor, and how has the drug discovery process been simplified? Through a data science course, you will study these phenomena and how they work.
Data science is a vast field comprising many Statistics, Mathematics, and IT topics. A data science course syllabus covers basic and advanced concepts of data analytics, statistics, machine learning, and programming languages like R or Python. It teaches how to interpret large datasets and identify patterns to create predictive models.
If you want to pursue a data science course and are looking for a data science course syllabus, this article is for you.
What is Data Science?
Data science course syllabus blends programming and domain expertise skills and mathematics and statistics knowledge to extract meaningful insights from data. The practitioners apply machine learning algorithms to images, numbers, text, video, audio, and more to produce artificial intelligence systems to perform tasks that require human intelligence. These systems generate insights that analysts and business users can translate into tangible business value.
Every single data science model goes through a life cycle of roughly 6 following steps:
- Data Acquisition: Collecting data based on the business problem.
- Data Preparation: Includes cleaning, transformation, processing, staging, and data architecture.
- Data Modeling: Defining, refining, and data classification. Use of ML techniques to identify the best business model.
- Visualization and Communication: Involves Business Intelligence(BI) and decision-making.
- Deploying the Model: Testing the model before deploying it for communal use.
- Exploratory Data Analysis (EDA): Realtime exploratory analysis after model deployment. Includes maintaining the model’s performance through regular qualitative and predictive analysis, text mining, and regression to monitor the business model’s smooth functioning.
Data Science Course Syllabus Highlights
|Course Name||Data Science|
|Course Type||Bachelor’s, Master’s, Diploma, Certificate Course, and online courses|
|Duration||Fluates between 3 months to 3 years|
|Admission Process||Entrance Exam|
|Eligibility||UG (10 + 2 from a recognized board)PG (Bachelor’s Degree)|
|Fee Structure||UG: ₹1,00,000PG: ₹ 2,00,000|
|Career Options||Data Science Manager, Data Science Consultant, Data Architect, and Data Scientist|
|Average Salary||₹ 12 lakhs per annum|
|Recruiting Options||Amazon, Deloitte, Flipkart, Accenture, IBM, Apple, Cisco and Google|
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Data Science Course Syllabus
Data Science course syllabus has three main components, i.e., Big Data, Machine Learning, and Modelling in Data Science. Across these three main components, the subjects cover varied areas of this sought-after discipline. Here are the complete Data Science course topics:
|Introduction to Data Science||Mathematical & Statistical Skills||Machine Learning|
|Coding||Algorithms used in Machine Learning||Statistical Foundations for Data Science|
|Data Structures & Algorithms||Scientific Computing||Optimization Techniques|
|Data Visualization||Matrix Computations||Scholastic Models|
|Experimentation, Evaluation, and Project Deployment Tools||Predictive Analytics and Segmentation using Clustering||Applied Mathematics and Informatics|
|Exploratory Data Analysis||Business Acumen & Artificial Intelligence|
Components in Data Science Course Syllabus
Given below are the key parts of the Data Science course syllabus:
1. Machine Learning & Deep Learning
Machine language converts human-understandable data into machine-interpretable code values, achieved using algorithms and artificial intelligence (AI). Deep learning, a subdivision of machine learning, is precise and applies self-reliant algorithms with fewer inputs.
This subject focuses on and expects you to be well-informed about algorithms like linear regression, logical regression, clustering, decision trees, etc. The connection between neural networks, algorithms, and libraries is studied here.
- Big data technology: All the leading firms handle big data and require analytics. Big data technologies write maps and produce codes. Hadoop HDFS and Apache Spark are the most prevalent big data technologies.
- Data Ingestion and Data Munging: With data ingestion, data is imported through channels to manipulate, analyze, or store. Apache Flume and Apache Corp are the tools used for this purpose. Any change made using artificial algorithms upon raw data for more precise visualization is data munging. Python and R packages are used for this. Data selection and removal are done through analysis, and discrepancies are corrected.
- Visualization: The most critical part is organizing and presenting the analyzed data. No technology algorithm or code can be shown, but an understandable report should be submitted. Report creation training and techniques are learned under this sub-topic.
- Problem-solving: The course aims to solve the business problem through data sets. To make a final call on the issue, the examined characteristics of the given raw data must be considered. For example, if a specific value in the data is high, the deviation’s impact is analyzed, and a perfect strategic solution is arrived at to overcome the variance. Hands-on experiences and case studies are given in the problem-solving section under the data science course.
2. Probability & Statistics
- Locate what the data conveys or implies by analyzing it, the core of decision-making. Careful data analysis to arrive at meaningful solutions to business problems should be achieved by the following systems.
- Basic mathematics analyzes mean, median, mode, standard deviation, and variance data.
- Probability distributions such as Poisson distribution, binomial distribution, etc.
- Various theorems and equations are applied to manipulate the data according to the case.
- Calculate the deviation of data from the standardized or normalized value through curve analysis.
All data cannot be jotted down on paper or Excel sheets as it is impossible to handle data when it parses a fixed volume.
So, programming languages are necessary to fetch sets of data to be analyzed or manipulated via selection criteria by writing relevant codes. R, Python, and Saas are data science’s most commonly used programming languages.
- Python: It is a simple, powerful, and user-friendly language. It is platform-independent, which enables the usage of Python in Linux, Windows, and Mac environments. Coding in Python is easy to learn, and the keywords imply the same English meaning. Python libraries like Apache, Expat, Dlib, and many more have details about machine learning, medical arrays, and visualization. They build statistical models for further studies.
- R: It is an open-source programming, statistical, and well-documented software language, thus being easy to learn. It is highly cost-effective with solid statistical capabilities.
- Saas: The expansion of Saas is a Statistical Analytical System, which means it is designed to perform statistical operations. It has an excellent GUI and accommodates operations in developing commercial business software. Saas imbibes many built-in codes to reduce coding time and effort. It stands unique from R and Python by being a fourth-generation language.
4. Database knowledge
The programming language fetches and works on the stored data. It obviously cannot fit into the office tools, so the employment of databases comes into the picture.
Data Science Course Syllabus for Beginners
For a beginner in data science, numerous introductory courses are available to familiarise yourself with the basics. An overview of the data science course syllabus for beginners is as follows:
|Introduction to Data Science||Understanding Exploratory Data Analysis||Machine Learning|
|Model selection and evaluation||Data Warehousing||Data Mining|
|Data Visualization||Cloud Computing||Business Intelligence|
|Storytelling with Data||Communication and Presentation|
Data Science Skillset Requirement
Data science course syllabus also includes all the skills required for the students, and they must be familiar with calculus, linear algebraic equations, graphs, and other mathematics tools. Knowledge of calculus and mathematics is a plus point for students in their recruitment procedures.
|Calculus and Mathematics||Communication Skills||Problem-solving Skills|
Data Science Course Syllabus Tools & Technologies
Being technical, data science demands proficiency in the practical aspects of the subject. Various tools and technologies are available that a data science pro must have expertise in to get better jobs in the field.
Some of the most in-demand data science tools and technologies are:
|SAS||Provides statistical libraries to help with modelling and organization|
|Apache Spark||An analytics engine to help Stream Processing|
|BigML||Used for processing of Machine Learning Algorithms|
|Matlab||Used for processing Mathematical information and simulating neural networks|
|Excel||Used for Data Analytics|
|ggplot2||Advanced data visualization tool used with R programming|
|Tableau||Data visualization software|
|Jupyter||An open-source tool also used in making predictive ML models|
|Matplotlib||Plotting and visualizing tools mostly used for graphs|
|NLTK||A Natural Language Processing Tool|
|Scikit-learn||A library mostly used to implement ML Algorithms|
|Weka||ML software based in Java|
Interview Questions for You to Prepare for Jobs
Data Science Course Eligibility
1. For Undergraduate level
- Students need to have completed their higher secondary education from a recognized board.
- Students must meet the data science course eligibility to pursue data science courses at top universities.
- Candidates must be from the science stream, with subjects such as mathematics, physics, and chemistry as their core subjects.
- Students must be qualified to clear the relevant entrance exams to get admitted to the best college or university.
- Aspirants must be above 17 years of age to pursue Data Science courses.
2. For Postgraduate level
- Students must have completed postgraduation in science with relevant specializations or any equivalent degree from a recognized board or university.
- Candidates must fulfill the data science course eligibility for pursuing postgraduate courses in data science.
- Students should qualify in the relevant entrance exam and have scored 50 percent marks in the qualifying examination.
- Students with prior qualifications, specialization, or experience in Mathematics / Statistics are preferred.
- Students also need to qualify and perform well in the personal interview rounds that may take place depending upon different colleges and universities after the entrance exam.
Data Science Course Duration
The duration of course, ranges from about 10 weeks to 5 years. Each course provider or university offers different durations. A proper data science degree may take about 20 months.
If the aspirant is willing for additional effort, the course could be completed in 12 months. There are short-term courses completed in about 6-10 weeks. Professionals willing to gain certifications may find it helpful.
Data Science Course Fees
The fee structure for the Data Science course may vary from institute to institute and depends on whether you are going for an online or offline system. Below is the Data Science course fee structure at different levels:
- Under Graduate: ₹32,000 to 35.64 lakhs
- Post Graduate: ₹15,000 to 13.60 Lakhs
- Doctoral: ₹31.50 lakhs
- Diploma: ₹45,000
Data Science Course Salary
Below is a table showing the average salary per annum offered in different job roles in the field of data science:
|Job Profile||Average Annual Salary|
|Data Scientists||₹ 7 LPA|
|Lecturer||₹ 6 LPA|
|Data Engineer||₹ 5 LPA|
|Data Architect||₹ 4 LPA|
|Data Analyst||₹ 3 LPA|
|Business Analyst||₹ 5 LPA|
|Business Intelligence Professional||₹ 2 LPA|
|Big Data Analyst||₹ 4LPA|
Most Recommended Data Science Books
Practical Statistics for Data Scientists by Andrew Bruce and Python Data Science Handbook by Jacob T. Vanderplas are the top 2 books recommended for aspiring data scientists. The table lists the best data science books for beginners and professionals in data science.
|Python Data Science Handbook||Jake VanderPlas|
|Practical Statistics for Data Scientists||Peter Bruce, Andrew Bruce & Peter Gedeck|
|Introducing Data Science||Davy Cielen, Anro DB Meysman, Mohamed Ali|
|The Art of Statistics Learning from Data||David Spiegelhalter|
|Data Science from Scratch||Joel Grus|
|R for Data Science||Hadley Wickham & Garrett Grolemund|
|Think Stats||Allen B Downey|
|Introduction to Machine Learning with Python||Andreas C Muller & Sarah Guido|
|Data Science Job: How to Become a Data Scientist||Przemek Chojecki|
|Hands-on Machine Learning with Scikit-Learn and TensorFlow||Aurelien Geron|
Data Science Course Benefits
Data Science course brings along several benefits with it. Curious to know what they are? Let us go down to see one by one.
1. Certifies you with in-demand Big Data technologies
Data Science course prepares for the growing demand for big data skills and technology. It empowers experts with data management technologies like Hadoop, R, Sqoop, Flume, ML, Mahout, etc. The knowledge and expertise of the skills are an added advantage for a better and more competitive career.
2. A flight to a better career path
The need for Data Science specialists is enormous in all the leading industries. Many top business locations in the world are offering data science jobs. You can easily make it by gaining data science expertise, skills, and technology through a data science course.
3. A ticket to get employed in the top Fortune companies
The list of companies looking for data science experts is endless. Data Science course is a way to get the expertise and skill to come on your resume, a ticket to the top fortune companies.
4. Qualifies you to occupy new positions
Big Data and analytics adoption among larger employers is vast and will only double in the future.
Data Science Course Syllabus PDF
On completing the introductory data science course syllabus, learners will gain a fundamental knowledge of data science principles and techniques. It empowers to make data-informed decisions and develop their data skills. With the appropriate resources and dedication, learners can become Data Science experts and use data to make a real impact.
Data science is an ever-evolving field. Therefore, data scientists must stay up-to-date on the latest data science trends and technologies. Learners must seek out data science-related courses, conferences, or professional certifications to further increase their knowledge of data science principles and techniques.
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FAQs on Data Science Course Syllabus
Data Science isn’t just a subject but a complete process that requires knowledge of several domains, including Computer Science, Mathematics, Statistics, and Business. Big Data, Machine Learning, Data Analysis, Business Intelligence, Programming languages like Python, R, and SQL, Data Visualization, and Data Modelling are a few of the most essential Data Science subjects.
The Data Science field rewards you almost better than any other field. However, it asks you to follow a specific career path to be a worthy data scientist.
– Bachelor’s degree
You must have a bachelor’s degree in Computer Science, Information Technology, or Mathematics.
– Entry-Level Job
After the degree, you must get a starter job as a data analyst or a junior data scientist for experience before entering the big games.
– Master’s degree
The Data Science field requires at least a master’s degree or a Ph.D. to get more immense opportunities. You can pursue a master’s parallelly with your entry-level job, too.
– Get a Promotion
Once you are done with your studies, there is no one stopping you from applying for higher opportunities.
Coding is an essential part of data science. However, some familiarity with programming can help understand concepts better. So, one can start with basic programming concepts. Gradually, a person can learn the necessary programming languages required. Many data science courses offer introductory programming courses. It helps learners build a foundation.
In India, a data scientist earns around ₹698,412 per annum on average. A newbie or an entry-level data scientist with less than 1 year of experience earns about ₹5,00,000 per annum, while a data scientist with at least 4 years of experience earns ₹6,10,811 per annum.
A mid-level data scientist having an experience of 5 to 9 years earns ₹10,04,082 per annum in India. The salary rises dramatically with your experience as a senior-level data scientist, around more than ₹17,00,000 a year in India.
You need a bachelor’s degree in appropriate fields, such as mathematics, computer science, computer applications, or something comparable, to be eligible for a master’s degree. Having a foundation in science is beneficial for beginners. You can choose a profession in data science if your educational background is in a quantitative field, such as finance or business management. When beginning a Data Science course, prior experience with simple analytics tools like SQL, Excel, or Tableau can be beneficial for students who have non-technical backgrounds.