In a data science course, you will learn a variety of concepts, techniques, and skills that are essential for working with and analyzing data.
You will learn key concepts such as statistics and probability, Python programming, data manipulation and cleaning, data visualization, machine learning algorithms, exploratory data analysis, data modelling and evaluation, big data processing, ethical considerations, and effective communication of data insights.
- Introduction of data science
- Data Science life cycle
- Tools of data science
- Introduction to Python
- Introduction to Machine Learning
- Introduction to Python Programming
- Basic operation
- Variable assignment
- Functions in python
- Condition and loops
- Data structure
- Array, Object, Exception handling
- File handling
- Introduction of NumPy and pandas in Libraries in python
- Installation of Pandas and NumPy libraries
- Data visualization libraries like (MATPOTLIB and SEABORN)
- Introduction to Power BI
- Introduction to SQL
- Fundamentals of Structured Query Language
- SQL Tables, Joins, Variables
- SQL Functions, Subqueries, Rules, Views
- Nested Queries, string functions, pattern matching
- Mathematical functions, Date-time functions, etc.
- Introduction of Statistics
Statistical Function like (Mean , Median , Mode)
- Measure of central tendency, measure of spread, five points summary
- EDA parts (Data Wrangling)
- Correlation Matrix
- Normal Distribution , Skewness , bell curve
- Probability Distributions, bayes theorem, central limit theorem
- Introduction to Machine Learning and categories
- Data Preprocessing
- Data Preparation
- Machine Learning types and algorithms
- Introduction Parameter in Machine Learning and PCA
- Introduction on Github and Kaggle
- Implementation of case study
- Git Lifecycle, Git commands, Working with branches
- Github collaboration (pull request), Github Authentication (SSH and HTTP)
- Merging branches, Resolving merge conflicts, Git workflow
- Recommendation Engine
- Housing & Census
- Object Detection
- Stock Market Analysis
- AI Chatbot
|Maximum Intake||15 Students|
|Batch Timing||7:30 to 9:30 AM|
|Experience Level||Data Scientist Salary||Data Analyst Salary|
|Entry Level||₹5 Lakhs/annum||₹3.4 Lakhs/annum|
|1-4 Years Expereince||₹5-6 Lakhs/annum||₹4.22 Lakhs/annum|
|5-9 Years Experience||₹10 Lakhs/annum||₹6.9 Lakhs/annum|
While not mandatory, a solid foundation in mathematics, statistics, and programming can be helpful. Strong analytical thinking, problem-solving abilities, and a curiosity to explore and analyze data are also important.
Data science courses usually cover topics such as data analysis, statistical modelling, machine learning, data visualization, data wrangling, and programming languages commonly used in data science like Python or R.
Prerequisites can vary depending on the course, but some courses may require a basic understanding of statistics and programming concepts. It’s advisable to check the specific prerequisites mentioned by the course provider.
The duration of data science courses can vary. It can range from a few weeks for introductory courses to several months or even a year for comprehensive programs. It depends on the depth and breadth of the course content.
While both fields involve working with data, data science focuses on extracting insights, building predictive models, and developing algorithms, whereas data analytics primarily focuses on exploring and interpreting data to support decision-making.
Consider factors such as your current skill level, career goals, course content, instructor expertise, hands-on exercises or projects, and student reviews. Research different course options to find the one that aligns with your learning objectives and interests.