Data Science with Python Course

Course Detail

Data Science with Python

Data Science with Python

This Data Science with Python course gives you a complete overview of Python’s data analytics tools and techniques. Learning python is a crucial skill for many data science roles, and you can develop it with this Python data science course. With a blended learning approach, you can learn Python for data science along with concepts like data wrangling, mathematical computing, and more. Unlock your career as a data scientist with Oxford Global Academy of Excellence.

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Module 1
  • Introduction to Data Science
  • Learning Objectives
  • Data Science Methodology
  • From Business Understanding to Analytic Approach
  • From Requirements to Collection
  • From Understanding to Preparation
  • From Modeling to Evaluation
  • From Deployment to Feedback
  • Key Takeaways
Module 2
  • Python Libraries for Data Science
  • Import Library into Python Program
  • Numpy
  • Demo Numpy
  • Fundamentals of Numpy
  • Numpy Array Shapes and axes Part A
  • Numpy Array Shapes and axes Part B
  • Arithmetic Operations
  • Conditional Statements in Python
  • Common Mathematical and Statistical Functions in NumPy
  • Indexing and Slicing in Python Part A
  • Indexing and Slicing in Python Part B
  • Introduction to Pandas
  • Introduction to Pandas Series
  • Querying a Series
  • Pandas Dataframe
  • Introduction to Pandas Pane
  • Common Functions in Pandas
  • Statistical Functions in Pandas
  • Date and Timedelta
  • IO Tools
  • Categorical Data
  • Working with Text Data
  • Iteration
  • Plotting with Pandas
  • Matplotlib
  • Demo Matplotlib
  • Data Visualization Libraries in Python Matplotlib
  • Graph Types
  • Using Matplotlib to Plot Graphs
  • Matplotlib for 3d Visualization
  • Using Matplotlib with Other Python Packages
  • Data Visualization Libraries in Python Seaborn An Introduction
  • Seaborn Visualization Features
  • Using Seaborn to Plot Graphs
  • Analysis using seaborn plots
  • Plotting 3D Graphs for Multiple Columns using Seaborn
  • SciPy
  • Demo Scipy
  • Scikit-learn
  • Scikit Models
  • Scikit Datasets
  • Preprocessing Data in Scikit Learn Part
  • Preprocessing Data in Scikit Learn Part
  • Preprocessing Data in Scikit Learn Part
  • Demo Scikit learn
  • Key Takeaways
Module 3
  • Introduction to Linear Algebra
  • Scalars and vectors
  • Dot product of Two Vectors
  • Linear Independence of Vectors
  • Norm of a Vector
  • Matrix
  • Matrix Operations
  • Transpose of a Matrix
  • Rank of a Matrix
  • Determinant of a matrix and Identity matrix or operator
  • Inverse of a matrix and Eigenvalues and Eigenvectors
  • Calculus in Linear Algebra
  • Importance of Statistics for Data Science
  • Common Statistical Terms
  • Types of Statistics
  • Data Categorization and types of data
  • Levels of Measurement
  • Measures of central tendency mean
  • Measures of Central Tendency Median
  • Measures of Central Tendency Mode
  • Measures of Dispersion
  • Variance
  • Random Variables
  • Sets
  • Measure of Shape Skewness
  • Measure of Shape Kurtosis
  • Covariance and corelation
  • Basic Statistics with Python Problem Statement
  • Basic Statistics with Python Solution
  • Probability its Importance and Probability Distribution
  • Probability Distribution Binomial Distribution
  • Binomial Distribution using Python
  • Probability Distribution Poisson distribution
  • Poisson distribution Using Python
  • Probability Distribution Normal Distribution
  • Probability Distribution Uniform Distribution
  • Probability Distribution Bernoulli Distribution
  • Probability Density Function and Mass Function
  • Cumulative Distribution Function
  • Central Limit Theorem
  • Bayes Theorem
  • Estimation Theory
  • Point Estimate using Python
  • Distribution
  • Kurtosis Skewness and Student's T- distribution
  • Hypothesis Testing and mechanism
  • Hypothesis Testing Outcomes Type I and II Errors
  • Null Hypothesis and Alternate Hypothesis
  • Confidence Intervals
  • Margin of Errors
  • Confidence Levels
  • T test and P values Using Python
  • Z test and P values Using Python
  • Comparing and Contrastin T test and Z-tests
  • Chi Squared Distribution
  • Chi Squared Distribution using Python
  • Chi squared Test and Goodness of Fit
  • ANOVA Terminologies
  • Assumptions and Types of ANOVA
  • Partition of Variance
  • F-distribution
  • F Distribution using Python
  • F-Test
  • Advanced Statistics with Python Problem Statement
  • Advanced Statistics with Python Solution
  • Key Takeaways
Module 4
  • Data Exploration Loading Files Part A
  • Data Exploration Loading Files Part B
  • Data Exploration Techniques Part A
  • Data Exploration Techniques Part B
  • Seaborn
  • Demo Correlation Analysis
  • Data Wrangling
  • Missing Values in a Dataset
  • Outlier Values in a Dataset
  • Demo Outlier and Missing Value Treatment
  • Data Manipulation
  • Functionalities of Data Object in Python Part A
  • Functionalities of Data Object in Python Part B
  • Different Types of Joins
  • Key Takeaways
Module 5
  • Learning Objectives
  • Introduction to Feature Engineering
  • Encoding of Categorical Variables
  • Label Encoding
  • Techniques used for Encoding variables
  • Key Takeaways
Module 6
  • Learning Objectives
  • Types of Plots
  • Plots and Subplots
  • Assignment 01 Pairplot Demo
  • Assignment 02 Pie Chart Demo
  • Key Takeaways
Module 7
  • Feature Selection
  • Regression
  • Factor Analysis
  • Factor Analysis Process
  • Key Takeaways

Career Opportunities

  • Data Scientist
  • Data Analyst
  • Data Engineer
  • Data Architect
  • Business Intelligence Analyst

Entry Qualification

  • Candidates will be admitted on the basis of interviews and / or group discussions.
  • 20% of the total seats will be reserved for SC, ST and OBC candidates.If the reserved seats are not filled within the specified period, the vacant seats will be offered to the general candidates.

Course Features

Industry Experienced Trainer
4.9 (Google Review)
Study Mode
Offline & Online
4 month
English, Bengali, Hindi
100% Job Assistance
Free & Paid
Course Price
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