Complete Data Engineering with Implemented Projects

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Hi All,

In today's newsletter, we delve into the fascinating world of data engineering.

Data engineering forms the backbone of any successful data-driven organization, serving as the foundation for efficient and reliable data management. It encompasses the processes and tools required to acquire, transform, store, and deliver large volumes of data to support analytics, machine learning, and other data-driven initiatives. Data engineers play a crucial role in designing and implementing data pipelines, ensuring data quality and integrity, and optimizing data storage and retrieval.

Projects Videos —

All the projects, data structures, SQL, algorithms, system design, Data Science and ML , Data Analytics, Data Engineering, , Implemented Data Science and ML projects, Implemented Data Engineering Projects, Implemented Deep Learning Projects, Implemented Machine Learning Ops Projects, Implemented Time Series Analysis and Forecasting Projects, Implemented Applied Machine Learning Projects, Implemented Tensorflow and Keras Projects, Implemented PyTorch Projects, Implemented Scikit Learn Projects, Implemented Big Data Projects, Implemented Cloud Machine Learning Projects, Implemented Neural Networks Projects, Implemented OpenCV Projects,Complete ML Research Papers Summarized, Implemented Data Analytics projects, Implemented Data Visualization Projects, Implemented Data Mining Projects, Implemented Natural Leaning Processing Projects, MLOps and Deep Learning, Applied Machine Learning with Projects Series, PyTorch with Projects Series, Tensorflow and Keras with Projects Series, Scikit Learn Series with Projects, Time Series Analysis and Forecasting with Projects Series, ML System Design Case Studies Series videos will be published on our youtube channel ( just launched).

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Github - Complete Data Engineering with Projects

Scripting and Automation

Shell Scripting

ETL ( Extract, Tranform and Load) basics

Why ETL is important?

How ETL works

ETL Tools

Relational Databases and SQL

Basic SQL

Advanced SQL

NoSQL Data bases and Map Reduce

Data Warehouses

Data Lakes

Structured Data

Semi Structured Data

Unstructured Data

Data Mart

Map-Reduce

Data Analysis

Pandas

Numpy

Advanced Pandas Techniques

Data Pre-processing

Handling missing values

Data Cleaning

Mean/mode/median Imputation

Hot Deck Imputation

Rescale Data

Binarize Data

Regression Imputation

Stochastic regression imputation

Feature Scaling

Data Augmentation

Read and Process Large Datasets

Data Visualization basics

Data Visualization Projects

Data Visualization using Plotly and Bokeh

Data Profiling

Summary Functions

Indexing

Grouping

Linear Regression

Multi Linear Regression

Polynomial Regression

Regression

Support Vector Regression

Decision Tree Regression

Random Forest Regression

Feature Engineering

GroupBy Features

Categorical and Numerical Features

Missing Value Analysis

Fill the missing Values

Unique Value Analysis

Univariate Analysis

Bivariate Analysis

Multivariate Analysis

Correlation Analysis

Spearman’s ρ

Pearson’s r

Kendall’s τ

Cramér’s V (φc)

Phik (φk)

Data Processing Techniques

Batch Processing

Stream Processing

Apache Spark

Apache Spark Commands

Apache Kafka

How Apache Kafka works

Big Data

Big Data

Types of Big Data

Big data tools

SQL and NoSQL Databases

Hadoop

Hadoop HDFS

Hadoop Yarn

Hive

Zookeeper

Pig

Cassandra

Sqoop

Data Pipelines and WorkFlows

Data Pipelines

Transformation

Processing

Workflow

Monitoring

Airflow

DAG

Infrastructure

Docker

Docker vs Virtual Machines

Most important Docker commands

Kubernetes

Snowflake

Power BI

Power BI

Which chart to use and When?

Power BI — Data Analysis Expressions

Joins

Data Profiling

Cloud Data Engineering

Data Engineering on cloud

AWS

AWS Services

Google Cloud Platform

Google Cloud Platform services

Machine Learning Algorithms

Linear Regression

Logistic Regression

Decision Trees

Random Forest

Support Vector Machines

K Nearest Neighbors

K means Clustering

Hierarchical Clustering

Neural Networks

Stay tuned for more exciting content in our upcoming newsletters!

Happy Learning!

-Team Ignito