Join the Best Institute for Data Science Course in Delhi (Uttam Nagar, Dwarka Mod, Janakpuri)
Data Science Using Python
ObjectiveThis course will teach the learner the advance part of the Python programming environment, including lambdas, reading and manipulating CSV files, and the NumPy module. The course will cover data manipulation and cleaning techniques using the popular Python pandas data science library, as well as the abstraction of Series and DataFrame as central data structures for data analysis, as well as tutorials on how to effectively use functions like groupby, merge, and pivot tables. Students will be able to take tabular data, clean it, alter it, and execute basic inferential statistical analyses by the end of this course.
This data science course will teach you how to clean, analyze, and visualize data using the Python programming language.
Course Duration1 And Half Month
EligibilityKnowledge Of Python Required
Syllabus
Syllabus
| Module Unit | Duration(Theory) in Hours | Duration(Theory) in Hours | Learning Objectives (Learner will learn after completion of unit) |
|---|---|---|---|
| 1. Python Language , Structures, Programming Contructs |
6 | 9 | -Write Programming in the Python Language. -Extensively use conditional |
| 2. Data Science Concept | 2 | 3 | 1. The concept of Data Science and Analytics and various steps to achieve analysis. |
| 3. Numpy | 8 | 12 | 1. Scientific computing and data analysis by understanding large mulitidimensional arrays and matrices 2. Run Efficient operations on arrays 3. Work on high-level mathemetical functions to operate on these arrays |
| 4. Pandas | 14 | 21 | 1. Data analysis after importing data from various sources. 2.understand the Series and DataFrame as the central data Structures for data analysis. 3.Learn various functions, grouping for data analysis. |
| 5. Statistical Concepts and Functions | 6 | 9 | 1. The Statistical tool of python having ability to manipulate some Statistical data and calculate results of various Statistical operations. 2.Understand functions like mean, median, mode and standard deviatin. 3.Understand the concept of Correlation and Regression. |
| 6. Matplotlib | 6 | 9 | 1. Learn the python library used to create graphs and plots with just a few commands. 2.Understand pyplot and its feature of line styles, font properties, formatting axes etc. 3.Understand all aspects of the programmatical control of all the figures |
| 7. GUI-Tkinter | 4 | 6 | 1. The standard python interface to the Tk GUI tookit for creating quick and intuitive GUI. 2.The various widgets for input and theie event handling. 3.Integrating the data analysis and graphs in Tkinter. |
| 8. Machine Learning | 2 | 3 | 1. Overview of Machine Learning and its concept |
| Module Unit | Written Marks (Max.) |
|---|---|
| 1. Python Language, Structure, Programming Contructs | 14 |
| Data Science Concepts | 6 |
| Numpy | 20 |
| Pandas | 24 |
| Statistical Concepts and Functions | 10 |
| Matplotlib | 10 |
| GUI-Tkinter | 12 |
| Machine Learning-The Next Step | 4 |
| Total | 100 |
Syllabus
| Module Unit | Duration(Theory) in Hours | Duration(Theory) in Hours | Learning Objectives (Learner will learn after completion of unit) |
|---|---|---|---|
| 1. Python Language , Structures, Programming Contructs |
6 | 9 | -Write Programming in the Python Language. -Extensively use conditional |
| 2. Data Science Concept | 2 | 3 | 1. The concept of Data Science and Analytics and various steps to achieve analysis. |
| 3. Numpy | 8 | 12 | 1. Scientific computing and data analysis by understanding large mulitidimensional arrays and matrices 2. Run Efficient operations on arrays 3. Work on high-level mathemetical functions to operate on these arrays |
| 4. Pandas | 14 | 21 | 1. Data analysis after importing data from various sources. 2.understand the Series and DataFrame as the central data Structures for data analysis. 3.Learn various functions, grouping for data analysis. |
| 5. Statistical Concepts and Functions | 6 | 9 | 1. The Statistical tool of python having ability to manipulate some Statistical data and calculate results of various Statistical operations. 2.Understand functions like mean, median, mode and standard deviatin. 3.Understand the concept of Correlation and Regression. |
| 6. Matplotlib | 6 | 9 | 1. Learn the python library used to create graphs and plots with just a few commands. 2.Understand pyplot and its feature of line styles, font properties, formatting axes etc. 3.Understand all aspects of the programmatical control of all the figures |
| 7. GUI-Tkinter | 4 | 6 | 1. The standard python interface to the Tk GUI tookit for creating quick and intuitive GUI. 2.The various widgets for input and theie event handling. 3.Integrating the data analysis and graphs in Tkinter. |
| 8. Machine Learning | 2 | 3 | 1. Overview of Machine Learning and its concept |
Marks Distribution
| Module Unit | Written Marks (Max.) |
|---|---|
| 1. Python Language, Structure, Programming Contructs | 14 |
| Data Science Concepts | 6 |
| Numpy | 20 |
| Pandas | 24 |
| Statistical Concepts and Functions | 10 |
| Matplotlib | 10 |
| GUI-Tkinter | 12 |
| Machine Learning-The Next Step | 4 |
| Total | 100 |
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