Certificate in Data Science
Scheme of the Course
| Slno | Paper Code | Paper Name | T | P | |
| 1 | DS01 | Introduction to Data Science and Ms Access | 60 | 40 | |
| 2 | DS02 | Programming Language:Python | 60 | 40 | |
| 3 | DS03 | Query Language:My SQL | 60 | 40 | |
| 4 | DS04 | Statistical Foundations for Data Science | 60 | 40 | |
| 5 | DS05 | Project | 60 | 40 | |
| 6 | DS06 | Total Marks | 500 | ||
| Introduction to Data Science and Ms Access: The fundamentals of data science include types of datasets and standard techniques for exploring data. Ms Access | |||||
Programming Language:Python: Python Basics
- Taking input in Python
- Python | Output using print() function
- Variables, expression condition and function
- Basic operator in python
- Data Types
- Strings
- List
- Tuples
- Sets
- Dictionary
- Arrays
- Loops
- Loops and Control Statements (continue, break and pass) in Python
- else with for
- Functions in Python
- Yield instead of Return
- Python OOPs Concepts
- Exception handling
Query Language:My SQL: Learn the basics of Structured Query Language (SQL) and how to query data from a relational database. You will also better understand other query languages, such as NoSQL and MongoDB.
MySQL SQLMySQL SELECTMySQL WHEREMySQL AND, OR, NOTMySQL ORDER BYMySQL INSERT INTOMySQL NULL ValuesMySQL UPDATEMySQL DELETEMySQL LIMITMySQL MIN and MAXMySQL COUNT, AVG, SUMMySQL LIKEMySQL WildcardsMySQL INMySQL BETWEENMySQL AliasesMySQL JoinsMySQL INNER JOINMySQL LEFT JOINMySQL RIGHT JOINMySQL CROSS JOINMySQL Self JoinMySQL UNIONMySQL GROUP BYMySQL HAVINGMySQL EXISTSMySQL ANY, ALLMySQL INSERT SELECTMySQL CASEMySQL Null FunctionsMySQL CommentsMySQL Operators MySQL Database
MySQL Create DBMySQL Drop DBMySQL Create TableMySQL Drop TableMySQL Alter TableMySQL ConstraintsMySQL Not NullMySQL UniqueMySQL Primary KeyMySQL Foreign KeyMySQL CheckMySQL DefaultMySQL Create IndexMySQL Auto IncrementMySQL DatesMySQL Views
Statistical Foundations for Data Science: Statistical Methods: Definition and scope of Statistics, concepts of statistical population and sample. Data: quantitative and qualitative, attributes, variables, scales of measurement nominal, ordinal, interval and ratio. Presentation: tabular and graphical, including histogram and ogives, consistency and independence of data with special reference to attributes. Measures of Central Tendency: mathematical and positional. Measures of Dispersion: range, quartile deviation, mean deviation, standard deviation, coefficient of variation, Moments, absolute moments, factorial moments, skewness and kurtosis, Sheppard’s corrections. Bivariate data: Definition, scatter diagram, simple, partial and multiple correlation (3 variables only), rank correlation. Simple linear regression, principle of least squares and fitting of polynomials and exponential curves. Index Numbers: Definition, construction of index numbers and problems thereof for weighted and unweighted index numbers including Laspeyre’s, Paasche’s, Edgeworth- Marshall and Fisher’s Ideal Index numbers. Errors in Index numbers. Chain index numbers, conversion of fixed based to chain based index numbers and vice-versa. Consumer price index numbers. Uses and limitations of index numbers.