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The course is designed to provide students with the essential skills to thrive in a work environment. It gives students practical and applicable skills for their professional growth, helping them succeed in any field that requires dealing with data.

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Faizan Khalid Mohsin
Data Scientist
3 Review
Categories
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Professional Training Course in Machine Learning and Data Science

Providing training equivalent to a junior data scientist & teaching the Data Science Software Development workflow cycle.

Overview

  • Lectures : 8
  • Duration : 60 hours
  • Language : R & Python
  • Students : 32
  • Assessments : Yes
Course Description

Data science has emerged as one of the most essential and popular fields of the 21st century. The popularity is due to the pressing need to analyze and construct insights from the immense amount of data being generated. from the data. Industries transform raw data into finished data products. In order to do so, it requires several important tools to churn the raw data. R and Python are the programming languages that provide an intensive environment for you to research, process, transform, and visualize information.

The R programming language was developed by statisticians to help other statisticians and developers to work faster and more efficiently with large amounts of data. Since machine learning almost always involves a large quantity of data and statistics is an integral part of data science, the use of R language is always recommended. Therefore, R is most useful for those working with huge quantities of data in machine learning and automation. Python is the principal programming language and the industry standard for machine learning and data science. Python is used for productionizing machine learning and data science code, as well as developing and Deploying applications.

Certification

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Learning Outcomes
  • Over 37 lectures and 55.5 hours of content!
  • LIVE PROJECT End to End Software Testing Training Included.
  • Learn Software Testing and Automation basics from a professional trainer from your own desk.
  • Information packed practical training starting from basics to advanced testing techniques.
  • Best suitable for beginners to advanced level users and who learn faster when demonstrated.
  • Course content designed by considering current software testing technology and the job market.
  • Practical assignments at the end of every session.
  • Practical learning experience with live project work and examples.cv

Curriculum

  • Week 1: Introduction to Software Stack
    • Lesson 1. Intro to Git and GitHub
    • Lesson 2. Intro to Stackoverflow
    • Lesson 3. Intro to Jira / GitHub Projects
    • Lesson 4. Project Management
    • Lesson 5. Scrum Principles
    • Lesson 6. Folder Structuring
    • Lesson 7. Install and Setup R & Python
  • Week 2: Introduction to Software Development
    • Lesson 1. Linux, Git Bash, Git Pull Requests
    • Lesson 2. R commands & Personalizing R
    • Lesson 3. R Mardown (+ running script)
    • Lesson 3. Flexdashboard Intro and in Action
    • Lesson 3. Dashboard Principles, Creation & Deployment
    • Lesson 3. Running Python in RStudio IDE
  • Week 3: DashBoarding and Exploratory Data Analysis
    • Lession 1. Power BI Dashboarding
    • Lesson 2. Dealing with Missing Values
    • Lession 1. Outlier Detection
    • Lesson 2. Manipulating & Grouping Data
    • Lession 1. Plotly, ggplot Visualizations
    • Lesson 2. Data Cleaning
    • Lession 1. Create EDA Report
    • Lesson 2. Make EDA Live App
  • Week 4: Machine Learning Concepts & Application
    • Lession 1. Prediction vs. Inference
    • Lesson 2. Training, Testing, and Validation data sets
    • Lession 1.Linear Regression
    • Lesson 2. Logistic Regression
    • Lession 1. Forcasting and Predition
    • Lesson 2. Model Assessment / Evaluation
    • Lession 1. Deploy ML models in Live App
  • Week 5: Dashboard in Action Part 1
    • Lession 1. Model Overfitting and Data Leakage
    • Lesson 2. Decision Trees
    • Lession 1. Random Forest
    • Lesson 2. XGBoost
    • Lession 1. Data Science Pipeline
    • Lesson 2. Common ML Algo Pitfalls
  • Week 6: Dashboard in Action Part 2
    • Lession 1. Custom Stylizing Using css
    • Lesson 2.Shiny Modules
    • Lession 1. Studnet Presentations

Instructor

Faizan Khalid Mohsin
Data Scientist

He is the CEO and Principal Data Scientist at Cube Statistica. He has been the lead statistician for several studies and has also worked as either a data scientist or digital solutions expert for several companies and institutions.

Technical Skills
Python.
DevOps.
Google Colab.
Databases.
Power BI.
Pandas.
TensorFlow.
Mediation Analysis.
Survival Analysis.
Clinical Trials.
Machine Learning.
Git & GitHub .

Great communication skills and extensive teaching experience in machine learning and statistics using Rand Python.