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python with data science

This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python. About this course: This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as group by, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses.

4Achievers is the best training institute in noida for Python with Data Science and Data Analytics

Course description

Lession 1 : Getting Set Up with the Analytical Python Ecosystem

Lession 2 : Basic Analytics with Python
Lession 3 : Numerical Analysis with Numpy
Lession 4 : Advanced Analytics with SciPy and sci-kit learn
Lession 5 : Tabular Data Analysis with Pandas
Lession 6 : Overview of Python Visualization Tools

1.1  Obtain the software
1.2 Explore the Python command line from IPython
1.3 Experiment and chronicle in the IPython Notebook

2.1 : Retrive data frin web
2.2 : Load,access and modify dictionaries of data
2.3 : Analyze data in Python dictionaries
2.4 : Understand advanced python analytics
2.5 : Visalize Python dictionary data with matplotlib

3.1 : Understand Numpy
3.2 : Understand Advanced Numpy
3.3 : Create Array of data in Numpy
3.4 : Access and modify array elements
3.5 : Compute on arrays
3.5 : Understand advanced NumPy

4.1 Understand SciPy
4.2 Compute means, medians, quartiles, and other statistics
4.3 Fit data and interpolate
4.4 Understand sci-kit learn

5.1 Understand Pandas
5.2 Use series objects
5.3 Understand dataframes objects
5.4 Handle missing data
5.5 Perform input and output
5.6 Understand advanced Pandas

6.1 Understand Python's graphical data exploration tools
6.2 Embed Python charts into graphical applications
6.3. Create web-based graphics from Python
6.4 Visualize 3D datasets using Python

Summary of Fundamentals of Data Analytics in Python Live Lessons


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