PYTHON FOR DATA SCIENCE
4.0 /5- Offered byAnalytixLabs
- Private Institute
- Estd. 2011
PYTHON FOR DATA SCIENCE at AnalytixLabs Overview
Duration | 130 hours |
Total fee | ₹25,000 |
Mode of learning | Online |
Credential | Certificate |
PYTHON FOR DATA SCIENCE at AnalytixLabs Highlights
- Earn a certificate after completion of the course
- 1 Assignment on basic Python
- 2 exercises for statistical analysis
- 4 case studies on Pandas for data munging & descriptive analysis
- 10 short exercises on NumPy & Pandas
PYTHON FOR DATA SCIENCE at AnalytixLabs Course details
- Candidates from different technical or quantitative backgrounds like Engineering, Finance, Maths, Statistics, Business Management who wish to start their career in Data Science and Machine Learning skills
- Python, which once was considered a general programming language, has emerged as a shining star of the Data Science world
- The key driver is the flexibility it offers for an end-to-end enterprise-wide analytics implementation, including machine learning and AI
- In this Python Data Science training, learner will learn data handling, visualization, statistical analysis, and predictive modelling
- This certification program is delivered by industry experts in both classroom & online training mode and includes tons of hands-on projects to help learner to develop professional-level competency
PYTHON FOR DATA SCIENCE at AnalytixLabs Curriculum
Visualizing Geospatial Data
Introduction to Folium
Maps with Markers
Choropleth Maps
Operations with NumPy (Numerical Python)
What is NumPy?
Overview of functions & methods in NumPy
Data structures in NumPy
Creating arrays and initializing
Reading arrays from files
Special initializing functions
Slicing and indexing
Reshaping arrays
Combining arrays
NumPy Maths
Python Essentials (Core)
Overview of Python- Starting with Python
Why Python for data science?
Anaconda vs. python
Introduction to installation of Python
Introduction to Python IDE's(Jupyter,/Ipython)
Concept of Packages - Important packages
NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc
Installing & loading Packages & Name Spaces
Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
List and Dictionary Comprehensions
Variable & Value Labels – Date & Time Values
Basic Operations – Mathematical/string/date
Control flow & conditional statements
Debugging & Code profiling
Python Built-in Functions (Text, numeric, date, utility functions)
User defined functions – Lambda functions
Concept of apply functions
Python – Objects – OOPs concepts
How to create & call class and modules?
Overview of Pandas
What is pandas, its functions & methods
Pandas Data Structures (Series & Data Frames)
Creating Data Structures (Data import – reading into pandas)
Cleansing Data with Python
Understand the data
Sub Setting / Filtering / Slicing Data
Mutation of table (Adding/deleting columns)
Binning data (Binning numerical variables in to categorical variables)
Renaming columns or rows
Sorting (by data/values, index) -By one column or multiple columns - Ascending or Descending
Type conversions
Setting index
Handling duplicates /missing/Outliers
Creating dummies from categorical data (using get_dummies())
Applying functions to all the variables in a data frame (broadcasting)
Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc.)
Data Analysis using Python
Exploratory data analysis
Descriptive statistics, Frequency Tables and summarization
Uni-variate Analysis (Distribution of data & Graphical Analysis)
Bi-Variate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
Data Visualization with Python
Introduction to Data Visualization
Introduction to Matplotlib
Basic Plotting with Matplotlib
Line Plots
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