Data Science with Python

Become a data scientist with expertise in Python programming

Course Code : 0113

$2495

Overview

Harvard Business Review has termed data science as the sexiest job of the 21st century. And Python programming, in the recent years, has become one of the most preferred languages in the field of data science. When it comes to build machine learning systems, Python provides an ideally powerful and flexible platform.

Through a comprehensive, hands-on approach, Cognixia’s data science with Python training program provides learners with the opportunity to experiment with a wide variety of data science algorithms. The program integrates real-life exercises and activities throughout the training, helping you to ensure a promising career ahead.

Schedule Classes

Delivery Format
Starting Date
Starting Time
Duration

Live Virtual Classroom
Saturday, 20 July 2019
09:30 AM - 12:30 PM EST
14 Days (Sat - Sun)

Delivery Format
Starting Date
Starting Time
Duration

Live Virtual Classroom
Friday, 16 August 2019
10:30 PM - 01:30 AM EST
14 Days (Fri - Sat)

Looking for more sessions of this class?

Course Delivery

This course is available in the following formats:

Live Classroom
Duration: 14 days

Live Virtual Classroom
Duration: 14 days

What You'll learn

  • Basics of data science and statistics
  • Advanced and applied statistics in data science
  • Python programming for data science
  • Applied statistics concepts in Python
  • Data visualization and data analytics in Python
  • Machine learning concepts
  • Real-world machine learning and data science use-cases

Outline

  • What is data science?
  • Examples of data science project objective
  • What you need to learn?
  • What are the opportunities?
  • Who is more suitable?
  • What is expected?
  • Exploring more about data science and machine learning
  • Understanding CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology
  • Type of random variables – based on scale of measurement
    • Nominal
    • Ordinal
    • Interval
    • Ratio
  • Variance
  • Standard deviation
  • Normal distribution
  • Standard normal distribution and Z-score
  • Binomial distribution
  • Poisson distribution
  • Sampling
  • Inferential statistics
  • Sampling distribution
  • Central limit theorem
  • Central limit theorem exercises
  • Hypothesis and hypothesis testing
  • One tail and two tail test
  • P-value and level of significance
  • Type I and Type II errors
  • Examples and Q&A
  • Python – Anaconda, Spyder, Jupyter
  • Installation and configuration
  • List
  • Tuples
  • Dictionary
  • Matrices
  • If-Else loop control structure
  • For loop
  • While loop
  • Custom functions
  • Exception handling
  • Creating NumPy arrays
  • Accessing data elements
  • In-built NumPY functions
  • Slicing and dicing operations
  • Numeric operations
  • Creating Pandas DataFrame
  • Accessing data elements
  • Adding, removing columns/rows
  • Conditional data filtering
  • Joining the DataFrames
  • Aggregation operations
  • Reading data from files
  • Python installation environment setup
  • Python Pandas Script
  • Installation
  • Using Plot()
  • Using subplots()
  • Customizations
  • Scatterplot
  • Bar plot
  • Boxplot
  • Histogram
  • Installation
  • Steps to follow
  • Line & point plot
  • Scatter plot
  • Using Plotly with DataFrame
  • Customization using Marker and Layout
  • Bar plot
  • Histogram
  • Boxplot
  • Creating multiple plots together
  • MatPlotLib Plotly Installation
  • Python MatPlotLib Script
  • Python Plotly Script
  • Distribution plot
  • Histogram plot
  • Rug plot
  • KDE plot
  • Joint plot
  • Heatmap
  • Sampling
  • Simulating standard normal probability distribution
  • Calculating probabilities in SND
  • Simulating normal probability distribution
  • Binomial distribution exercise
  • Poisson distribution exercise
  • Hypothesis testing using Z-test
  • Hypothesis testing using T-test
  • Statistical analysis using Seaborn
  • What is machine learning?
  • Supervised and unsupervised machine learning models
  • Parametric and non-parametric models
  • Covariance and correlation
  • Regression
  • Linear regression
  • Linear regression methods
    • Ordinary least square
    • R-squared method
  • Why logistic regression
  • Logistic regression function
  • Machine learning model validation
  • Training and testing
  • Underfitting and overfitting
  • Confusion matrix
  • K-fold cross validation
  • Regression evaluation matrix
  • Machine Learning Case Study I – Special assistance area prediction (city with poor health conditions)
  • Classification using KNN (K Nearest Neighbor)
  • Machine Learning Case Study – Prostate cancer tumor classification
  • Case studies and Z-table
  • Decision Trees – classification and regression tree
  • Random Forest
  • Machine Learning Case Study – Regression using Random Forest
  • Support Vector Machine (SVM)
  • Clustering
    • K means clustering
    • Hierarchical clustering
  • Time Series Analysis
  • ARIMA Time series Models
    • AR
    • MA
    • ARMA
    • ARIMA
  • Machine Learning Case Study – Time Series Analysis using ARIMA
  • Case Study IV – Random Forest using Regression Tree (White Wine Quality Rating Prediction)
  • Case Study V – Cluster Analysis – Cab Driver Segmentation Analysis
  • Case Study VI – Time Series Analysis (ARIMA) – Energy Production Forecastinga
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Prerequisites

There are no prerequisites for this program; however, having a background in mathematics, statistics or computer science could be helpful.

Who Should Attend

This training program is highly recommended for current and aspiring –

  • Data Analysts
  • Data Scientists
  • Financial analysts
  • Software Developers
  • Programmers
  • Data Engineers
  • Python Developers
  • Data Architects
  • Software Engineers
  • Business Analytics Manager
  • Product Engineers
  • Data Analytics Engineers
  • Big Data Analysts

Interested in this course? Let’s connect!

Certification

Participants will be awarded with an exclusive certificate upon successful completion of the program. Every learner is evaluated based on their attendance in the sessions, their scores in the course assessments, projects, etc. The certificate is recognized by organizations all over the world and lends huge credibility to your resume.

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