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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

A Super Helpful Month-by-Month Plan to Become a Data Engineer in 2024

Sakshi Raheja
Sakshi Raheja Last Updated : 23 Feb, 2024
4 min read

Overview 

  • This article will help you to achieve your career goal to become a Data Engineer in 2022
  • We will take you through a learning path that will help you structurize your learning efforts to become a Data Engineer

Introduction

Data Science is still considered a contemporary and highly advanced field. Everyone wants to be associated with something which is very trending. Right? So, if you’re planning to become a Data Engineer, then I would like to congratulate you!

Data Engineering is an emerging field that is highly intertwined with the Data Science field with the heterogeneity of job opportunities. The market leaders like Google, Twitter, and Tesla are all generating data at an unprecedented pace. Just imagine, the amount of data that needs to be tackled on an everyday basis!

By adopting Big Data Technologies, the data engineers make sure that this huge volume of data that is coming in at a high velocity, can be transformed and transported to its destination where it needs to be stored properly and efficiently and put to use as and when required.

Data Engineering

With this much demand in the market, it is imperative to have a practical learning path to structurize and optimize your learning to become a valuable data engineer in 2022 and have a fruitful career.

This learning path has been adapted from the free course – A Comprehensive Learning Path to becoming a Data Engineer in 2022. You can check out the course to get all the resources structured according to the month-on-month plan.

 

Monthly Plan 

Below is the monthly plan which is going to be your go-to guide for the year 2022. If you follow our planner and are passionate, then by the end of the year, you will be able to begin a career as a Data Engineer.

January – Learn Programming

The first month of the New Year should definitely set the tone for your professional journey. You need to be self-disciplined and structured by devoting a fixed number of hours each day. So, let’s start with the basics and take one step at a time towards your Data Engineering Career. It is important for you to learn to program.

  • Learn programming in Python
  • Solve basic python problems on HackeRank

February – Understand Relational Databases

Let’s now move forward and try to understand the relational databases. It is important for you to get comfortable with writing SQL commands using MySQL.

The reason why we want you to start with MySQL is that it is the most commonly used RDBMS used by most of the giants in the industry.

March – Fundamentals of Linux and Cloud Computing

This month, we will learn the fundamentals of Linux and Cloud Computing. Focus areas for this month will be:

  • Basic Linux Commands
  • Basics of Cloud Computing

Month 4 –  NoSQL Databases

In the month of April, we will move to the NoSQL Databases where we will learn about:

  • Different types of NoSQL databases
  • Basic querying in MongoDB

Month 5 – Hadoop Ecosystem

Let us understand a very important open-source framework that is intended to make the interaction with Big Data easier. This framework is known as Hadoop Ecosystem.

  • Overview of the Hadoop Ecosystem
  • Understand MapReduce processing
  • Understand the working of YARN

Month 6 – Data Warehousing

Let’s dive deeper into the course and now learn data management system which is known as Data Warehousing. Here, we will focus on:

  • Understand the concept of Data Warehousing
  • Work with Hive Query Language in Apache Hive

Month 7 – Data Visualization

Now, we will learn about Data Visualization which is an interdisciplinary field that helps us to formulate large sets of data in form of charts and graphics. Organizations believe that it is one of the most attractive techniques to gauge customers’ attention. Well, we all tend to have long-lasting memories of the visuals as compared to the text. Isn’t it?

  • Learn About Tableau
  • Try connecting Tableau dashboards with any database and programming language

August & September – Apache Spark

We will cover one of the most relevant tools of the current times in these two months, Apache Spark. For those who don’t know, Apache Spark is a distributing processing system that is used for big data workloads. It helps to utilize the memory-catching and optimized query execution for fast analytic queries.

We will cover the below aspects of Apache Spark:

  • Understand Spark components and processing
  • Work with Spark RDDs and DataFrames

 October – Handling Streaming Data

With humongous data, it is important for Data Engineers to learn the art of handling streaming data. Here, you will learn:

  • Understand what is Data Streaming
  • Work with DStreams
  • Get acquainted with stateless and stateful transformations

November – Kafka

No matter how hard you work, the most important thing is to have the results in ‘real-time’. With the world moving so fast, we can’t afford to leave any opportunity to be the best. In this month, you will learn about Kafka which is used to build real-time streaming data pipelines and applications that adapt to the data streams.

  • Learn Kafka architecture
  • Create topics
  • Code your first producer and consumer

December – Airflow

Let’s quickly get equipped with Airflow before we say bye to 2022. Airflow is the python based open source orchestration tool. Here, you will learn about:

  • Learn about the airflow DAGs
  • Pass data between 2 different tasks
  • Build a project that includes most of the tools that you have studied

And, that’s a wrap with Airflow!

So, are you excited to begin your Data Engineering Career?

Here is an infographic of the monthly plan as in the Comprehensive Learning Path to becoming a Data Engineer in 2022

Data Engineering Career

Conclusion

Once you’re able to successfully complete this planner. You can definitely land into a career as a Data Engineer with a leadership role that will expect you to wear different hats on the go. So, once you complete this planner, I would recommend you to work on your communication skills, structured thinking ability, and overall personality development which will help you to land the right job! After all, recruiters are always on the lookout for perfection.

Once you finally get your dream job, don’t get complacent. Stay up-to-date with the relevant trends of the industry like this is one of the fastest-growing industries.

Comment below if you need a mentor who will be able to guide you in your professional journey! I’ll be happy to connect you with some of the experts from our team. You could also visit our website for courses.

Analytics Vidhyadata engineering careerdata engineering free coursemonthly planner
Sakshi Raheja
Sakshi Raheja

I am a passionate writer and avid reader who finds joy in weaving stories through the lens of data analytics and visualization. With a knack for blending creativity with numbers, I transform complex datasets into compelling narratives. Whether it's writing insightful blogs or crafting visual stories from data, I navigate both worlds with ease and enthusiasm. 

A lover of both chai and coffee, I believe the right brew sparks creativity and sharpens focus—fueling my journey in the ever-evolving field of analytics. For me, every dataset holds a story, and I am always on a quest to uncover it.

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Top 10 Data Analytics Projects with Source Codes
Top 10 Data Analytics Projects with Source Codes
Step-by-Step Roadmap to Become a Data Engineer in 2024
Step-by-Step Roadmap to Become a Data Engineer in 2024

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