How do I become a data scientist in India, What is the Data Science course subjects.
Join us to discover alumni reviews, ratings, and feedback, or feel free to ask any questions you may have!
Login to our social questions & Answers Engine to ask questions answer people’s questions & connect with other people.
Lost your password? Please enter your email address. You will receive a link and will create a new password via email.
Please briefly explain why you feel this question should be reported.
Please briefly explain why you feel this answer should be reported.
Please briefly explain why you feel this user should be reported.
As data science is in high demand and data science course subjects are a major need for the learners so that they have a clear understanding about data science. Data Science and its applications which is followed by a deeper dive into topics such as Data Collection, Data Visualization, Data Pre-processing, Machine Learning basic, Python Programming basics, Python Libraries required for Data Science along with Capstone Projects including Building a Movie Recommendation System, Cancer Prediction, etc. Following are some data science course subjects.
1. Statistics:
It is definitely among the most important data science course subjects. It involves maintaining records and important information. Statistics is the process of analyzing historical data, like customer search history.
1.1 Descriptive Statistics:
It helps to understand the data. By quantitative summarization of your data through numerical representations and graphs, data can be properly described. It includes Normal Distribution, Central Tendency, Kurtosis, and Variability.
1.2 Inferential Statistics:
Data is not of any use if you can’t draw a conclusion out of it. Making inferences about the population through a smaller sample can be done through this. The methods it includes are Central Limit Theorem, hypothesis testing, ANOVA and Quantitative Data Analysis.
2. Mathematics for Machine Learning:
It is also an important part of A.I and forms an important basis of data science course subjects. Machine Learning theory is a field that divides Linear Algebra and Calculus. With the right approach through the practical implementation of math, it can be quite fun!
3. Programming Knowledge: A good hold of programming languages
is a must in data science and it forms a basis in data science course subjects. These are the essential ingredients for Data science.
1)Advanced Microsoft Excel:
Excel is very useful to clean data because of its vast set of features.
2)Python:
Python is an interpreter based language as it interprets the Python code . You can check out over the internet for some good learning.
3)R:
R is customized to develop statistical models for analyzing a large amount of data.
4)SQL:
SQL helps to retrieve and manipulate data from the database.
In conclusion, I just want to say that even if you read 100 books, but are not able to apply that theoretical knowledge on numbers then it will not add value. Online courses are available and all you need to do is be self-motivated and inspired by yourself and you will see how you will be able to learn it in a short span of period.
These are some of the important data science course subjects which are required to make a career in data science.
Data science is the field of study that includes domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful perceptions from data.
3 Main subjects in the Data Science course are:
Machine Learning
Big Data
Business Intelligence
Machine Learning Course Syllabus
Machine learning is the execution of algorithms and mathematical models to make capable machines of solving problems like humans. In data science, machine learning can be used to predict the outcomes for the coming months or years based on past data.
Syllabus :
Introduction to Machine Learning
Machine Learning Techniques and Algorithms
Machine Learning and Artificial Intelligence
Programming Languages (Python, Java, C++, R, etc.)
Deep Learning
Artificial Neural Networks and their Application
Natural Language Processing
Reinforcement Learning
Big Data Course Syllabus
Big Data instruments and procedures are crucial in changing this unstructured data into a structured structure.
Syllabus of Big Data:
Basics of Programming
Agile Methodology
Testing and Version Control
Large-Scale Data Processing
NoSQL Databases
Integration and Testing
Object-Oriented Design
Big Data Fundamentals
ETL and Data Ingestion
Hive and Querying
Business Intelligence Syllabus
When broken down cautiously and introduced in visual reports such as graphs, the data delivered consistently by businesses can refreshen.
Quantitative Methods
Managerial Economics
Management Information Systems
Financial Accounting
Marketing Management
Organizational Behaviour
Statistical Analysis
Data Modelling
Predictive Analytics
Risk Management
Marketing Analysis
Managerial Communication
Data Mining
Simulation Modeling
Analytics, Systems Analysis & Design
Financial Management
Operations Management
Human Resource Management
Financial Analytics
Optimization Analytics
Stochastic Modeling
Business Intelligence
Research Methods
Computational Methods
Strategic Management
Operations & Supply Chain Analytics
HR Analytics
Big Data Analytics
Ethical & Legal Aspects of Analytics
Project Management
Data Science Syllabus for Beginners
Data science beginners or those who want to check out data science courses after the 12th can pursue data science courses online. There are plenty of online data science courses for beginners. The data science syllabus for beginners in the section below:
Introduction to Data Science
Data Mining
Cloud Computing
Data Analysis
Data Visualization
Data Model Selection and Evaluation
Machine Learning
Business Intelligence
Data Warehousing
Data Dashboards and Storytelling
Simply said, data science is an interdisciplinary field of study that uses scientific procedures, methods, systems, and algorithms to draw necessary conclusions and information from both structured and unstructured data. Big Data, ML, and Data Science Modeling are the three primary pillars of the data science curriculum. Statistics, coding, business intelligence, data structures, mathematics, machine learning, & algorithms are some of the key topics covered in the Data Science curriculum. The three main topics covered in the Data Science course syllabus are Big Data, Machine Learning, and Data Science Modeling. The contents span a variety of topics within these three key areas of this popular discipline. The whole Data Science Syllabus is available here.
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