Course details
Data science has become a great trend in computational and predictive statistical analysis. It’s also used by various organizations to make data-driven decisions. With all the challenges faced, Python has become an indispensable tool for the data science analyst and an important tool for any data scientist.
Vskills Certification Course in Data Science with Python will provide a means of transcending the theory of data science with the help of Python and many other integrated
… + Read MoreCourse details
Data science has become a great trend in computational and predictive statistical analysis. It’s also used by various organizations to make data-driven decisions. With all the challenges faced, Python has become an indispensable tool for the data science analyst and an important tool for any data scientist.
Vskills Certification Course in Data Science with Python will provide a means of transcending the theory of data science with the help of Python and many other integrated toolsets.
Why should one take Data Science with Python Certification?This Course is intended for Individuals wanting to understand a deeper level of data science using more advanced techniques and operations and individuals wanting to expand their knowledge of Python while learning data science; IT specialists aspiring to learn a new skill set; statisticians; computer scientists; and IT analysts etc.
Table of Contents
1. Introduction to Data Science- Introduction to data science
- What is Big Data
- Using Data Wisely
- Application Examples
- Data Science Process Flow
2. Python Essentials for Data Science- Installing python and text editor
- Introduction to python
- Python Objects
- Exercise 1
- Indexing & Slicing
- Python lists, tuples & dictionaries
- Mutable & Immutable Objects
- User-defined Functions
- Built-ins & Methods
- Control Flow Tools
- Modules, Libraries & Packages
- Introduction to Jupyter Notebook
- Essential Data Science Packages
3. Data ScienceToolBox - 1- Introduction to Numpy
- Creating a Numpy Array
- More Array Creation techniques
- Exercise 1
- Indexing, Slicing& Iterating with Numpy Arrays
- Exercise 2
- Introduction to Matplotlib
- Plotting with the plot function
- Histogram
- Scatter Plot
- Box Plot
- Adding a Legend
- Creating Subplots
- Lambda Functions
- List Comprehensions
4. Data Science ToolBox - 2- Introduction to Pandas
- Pandas Series
- Pandas DataFrames
- Pandas DataFrames 2
- Input-Output Tools
- Start your first Case Study
- Iterators
- Generators
5. Importing & Cleaning Data- Importing Flat Files
- Pickled Files
- Importing from a URL
- Using APIs & JSONs
- Inspecting Data Types
- Inspecting Outliers
- Finding Correlation & Duplicates
- Handling Missing Data
- Regular Expressions
6. Data Visualization with Statistical Thinking- Introduction to Seaborn Package
- SeabornDistplot
- SeabornDistplot 2
- SeabornJointplot
- SeabornHexplot
- SeabornPairplot
- Seaborn Boxplot
- SeabornViolinplot&Swarmplot
- SeabornCountplot
- Heatmap
- Exercise – Studying the feature correlation
- Feature Selection
- Feature Scaling
- Applying Feature Scaling & Feature Extraction
- Feature Extraction using PCA
to Machine Learning with Scikit-Learn- Introduction to Machine Learning
- Supervised & Unsupervised Machine Learning
- Classification
- Regression
- Clustering
- Scikit-Learn - Naïve Bayes Algorithm
- Scikit-Learn - Decision trees
- Scikit-Learn - Random Forest
- Scikit-Learn - Linear Regression
- Scikit-Learn - Logistic Regression
- Scikit-Learn - Support Vector Machines
- Scikit-Learn – kNN(K Nearest Neighbours)
- Scikit-Learn – K Means
8. Practice with Case Studies Updated on 01 February, 2019 - Read LessAbout Vskills
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