Career Path

Becoming a Data Engineer

Name: Becoming a Data Engineer
Brand: CodeRed
Price: 199.00 USD
Availability: InStock

Become a Data Engineer Without Spending a Fortune on Your Training!

38 hr 40 m of video content

Beginner

Validation of Completion Included

Buy this course now

$199.00

Overview What You Will Learn Curriculum Instructor Student Reviews

Course Overview

Become a Data Engineer Without Spending a Fortune on Your Training!

What You Will Learn

You'll learn Data Mining techniques and practical uses
You'll learn advance clustering and classification techniques
You'll get comfortable with the Python data analysis stack - Pandas
Matplotlib & Seaborn.
You'll be able to generate insights for ANY data analysis assignment
You'll learn how to process data with Pandas with some basic operations.
You'll learn how to work with Matplotlib and Seaborn
You'll learn to use R to analyze data and generate insights for business problems
You'll master the basic and advanced SQL syntax to extract meaningful data from databases

Program Curriculum

Content

Chapter 1: Introduction to Data Mining
Chapter 2: Essentials of Data Mining
Chapter 3: Analyzing Mining Technique and Algorithm
Chapter 4: Deep Dive into Clustering
Chapter 5: Deep Dive into Tree Classification
Chapter 6: Deep Dive into Handling Outlier and Effect
Chapter 7: Understanding Project Pipeline
Chapter 8: Advance Clustering Techniques

Content

Chapter 1: Introduction to the Course
Chapter 2: Setting up the Environment
Chapter 3: Introduction to NumPy
Chapter 4: Introduction to Pandas
Chapter 5: Data Wrangling and Visualisation

Content

Chapter 1: Case Study 1: Data Understanding and Insights
Chapter 2: Case Study 2: Analysis to Aid a Business Objective
Chapter 3: Next Steps

Content

Chapter 1: Introduction to Python and Setup
Chapter 2: NumPy and Visualization Basics
Chapter 3: Introduction to Pandas Data Library

Content

Chapter 1: Data Processing with Pandas
Chapter 2: Visualization with Matplotlib
Chapter 3: Working with Real Life Data
Chapter 4: Visualization with Seaborn

Content

Chapter 1: Introduction to the Course
Chapter 2: Data Visualisation & Reports
Chapter 3: Case Study 1
Chapter 4: Case Study 2
Chapter 5: Next Steps

Content

Chapter 1: Welcome
Chapter 2: Database Concepts
Chapter 3: SQL Basics
Chapter 4: Advanced SQL
Chapter 5: Data Analysis
Chapter 6: Conclusion

Content

Chapter 1: Introduction
Chapter 2: MySQL with Python
Chapter 3: SQLite with Python
Chapter 4: MongoDB with Python

Content

Chapter 1: Azure Data Factory - Fitment in data pipeline
Chapter 2: Implementing and Configuring Data Factory
Chapter 3: Working with Data Flow and Transformation
Chapter 4: Data Factory Monitoring

Content

Chapter 1: Introduction to Redshift and Cluster Management in Redshift
Chapter 2: Architectural Overview of Redshift Internals
Chapter 3: Data Warehouse Design Considerations using Redshift
Chapter 4: Loading Data into Redshift
Chapter 5: Securing Your Redshift Data
Chapter 6: Backup and Recovery of Redshift Data
Chapter 7: Performance Tuning of Redshift
Chapter 8: Key Takeaways from the Course

Content

Chapter 1: Introduction to Machine Learning
Chapter 2: Exploring Data Pre-processing
Chapter 3: Exploring Classification in Machine Learning
Chapter 4: Exploring Regression in Machine Learning
Chapter 5: Explore How to Fine-tune Models in Machine Learning
Chapter 6: Understand the Deployment of Machine Learning Models
Chapter 7: Introducing Deep Learning

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Instructor

Vivek Chaudhary

Vivek Chaudhary currently works as a freelance data scientist and has worked with different product-based & EdTech startups. He has published one of the best-selling books on Amazon, “Data Investigation-EDA the right way”. His areas of expertise are applied statistics, EDA, data cleaning techniques, and feature engineering and process to building statistical models. According to him “Building Assumptions” is the important factor to apply statistical tools in real-time. If you can’t build assumptions, then no matter how much you learn at the end, it will be difficult to apply statistical techniques. He has mentored 200+ professionals to start their journey and helped them understand applied statistics & EDA.

Ankit Shukla

Ankit Shukla is a self-taught data scientist working in the data space since 2014. He is well versed in deep learning, machine learning and Big Data technologies and holds a bachelor’s in engineering from Birla Institute of Technology, Mesra with a major in Biotechnology. His focus areas include Predictive Modelling, Recommender Systems and Natural Language Processing. He is deeply passionate about data science and is always on a look out to give back to the community. His other published works include a paper on developing an enterprise recommender system, 2 papers on Generative Adversarial Networks and the book - Big Data Analysis with Python.

Abdul Rehman

Abdul Rehman is a Machine learning engineer with years of industry-level experience for building power and intelligent machine learning applications, and the founder at Pythnist.org. Which is a place to learn Python technologies, including web development, machine learning, and Data Science.

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