Interview Prep
Career
GenAI
Prompt Engg
ChatGPT
LLM
Langchain
RAG
AI Agents
Machine Learning
Deep Learning
GenAI Tools
LLMOps
Python
NLP
SQL
AIML Projects

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

10 Must-Have Big Data Skills to Land a Job in 2024

A
Analytics Vidhya Last Updated : 12 Jun, 2024
8 min read

Introduction

In the rapidly evolving world of modern business, big data skills have emerged as indispensable for unlocking the true potential of data. This article delves into the core competencies needed to effectively navigate the realm of big data. Whether you are an aspiring data scientist, a seasoned IT professional, or a business leader, mastering data analysis, processing, and advanced machine learning techniques is vital to remain competitive and thrive in today’s data-driven era.

Learning Objectives

  • Understand what big data is.
  • Understand the importance of big data.
  • Get familiar with each skill important for a big data professional.

Table of Contents

What is Big Data?

The term big data is used when referring to an immense amount of data which is either unstructured, structured, or semi-structured. Data formats such as text, videos, photos, and social media posts are all included. This huge amount of data poses handling challenges for traditional data processing techniques. Big Data demands the employment of specialized storage, processing, and analysis equipment and techniques to efficiently deal with its 5 characteristics:

  • Veracity
  • Volume
  • Variety
  • Velocity
  • Value
Characteristics of Big Data
Source: Medium

Why is Big Data Important? 

Innovation and Product Development: Big Data fuels innovation by giving organizations a greater understanding of customer preferences, emerging patterns, and market trends. With this knowledge, they may develop unique solutions tailored to the demands of particular consumers.

Insights and Decision-Making: Big Data enables businesses to analyze and extract important insights from massive, diversified information. Businesses may make data-driven decisions, optimize processes, and gain a competitive advantage by identifying patterns, trends, and correlations.

Improved Efficiency and Productivity: Big Data analytics assists organizations in identifying inefficiencies, bottlenecks, and places for process improvement. Businesses can increase efficiency and productivity through resource allocation, optimizing operations, and supply chain management.

Risk Management and Fraud Detection: Big Data analytics is essential for detecting possible hazards, fraud tendencies, and abnormalities. Organizations may proactively detect and reduce threats by analyzing huge amounts of data in real-time, guaranteeing the security of money transactions and sensitive data.

Personalized Customer Experiences: Big Data helps businesses to collect and analyze customer data at scale. This data aids in developing targeted marketing campaigns, personalized experiences, and tailored suggestions, increasing consumer happiness and trust.

Scientific and Medical Advancements: Scientific research and medical advancements are being revolutionized by Big Data. Researchers can gain insights, identify new medicines, anticipate disease outbreaks, and enhance public health by analyzing enormous amounts of information.

Big Data Market Trend in 2024

Let us go through some top facts and statistics on the importance of big data:

  • The worldwide Big Data and Analytics market is valued at $274 billion.
  • Colocation data centers generate over $50 billion in income per year.
  • 2.5 (approx.) quintillion bytes of data are created on a daily basis.
  • 43% of IT decision-makers feel that their IT infrastructure would be unable to meet future data demands.
  • Big Data analytics in healthcare might be worth $79.23 billion by 2028.
  • The digital universe contains over 44 zettabytes of data.
  • End-user consumption of cloud computing is estimated to be approx. $500 billion per year.
  • 45% of firms outsource some of their Big Data workloads to the cloud.

Top 10 Big Data Skills to Have in 2024

In order to land a job in this rapidly changing industry, it is important to have the ten necessary big data skills that will make you stand out from other applicants.

  1. Problem-Solving 
  2. Programming Languages  
  3. Data Structures and Algorithms
  4. SQL and NoSQL Databases
  5. Data Warehousing
  6. Data Mining
  7. Distributed Frameworks
  8. Cloud Computing
  9. Interpretation and Data Visualization 
  10. Machine Learning

Problem-Solving

Big data specialists must possess excellent problem-solving skills to address challenges related to data quality, scalability, privacy, and computing efficiency. They need to devise creative solutions to optimize data processing procedures. Not only that, they also need strong quantitative analysis and analytical skills which are crucial for extracting valuable insights from massive datasets through statistical analysis, hypothesis testing, and mathematical modeling. These skills facilitate data-driven decision-making and pattern recognition.

An essential tool to master before learning any other big data technology to master problem-solving skills is Excel. Microsoft Excel is a simple yet effective analytical tool to store data and analyze it. Mastering Excel will help any big data professional to hone their problem-solving skills.

Programming Languages

Pprogramming languages are extremely important for any big data professional. Every big data engineer will be involved with designing and building pipelines. For this, it is crucial for a big data professional to know the ins and outs of programming.

There is not a set programming language that a big data professional needs to know. However, knowledge of any popular language from the likes of Python, Java, or Scala would be good enough.

Data Structure and Algorithms

Now, knowledge of programming languages is one thing but how to write efficient code is another. Writing efficient code is important to optimize the storage utilization to handle big complex data. For this, we have the concepts of data structures and algorithms which are the cornerstones of computer science.

These concepts are extremely important for big data professionals. For example, how would you sort an array containing millions of records? If you go by the brute force method, you will end up utilizing a lot of unnecessary resources. Or let’s say, how can you efficiently store a sparse array? To know the answers to such real-life problems you need to understand data structures and algorithms.

SQL and NoSQL Databases

Structured Query Language or SQL is a query language to manage data in a relational database. These databases store data in a structured format within rows and columns. MySQL and PostgreSQL are the two most popular SQL databases.

On the other hand, not all data can be incorporated strictly within a structured table. Also, SQL databases will not be suitable for all purposes as they lack data throughput speed and flexibility to store unstructured data. For these reasons, there are different kinds of databases known as NoSQL databases specially designed for such purposes. MongoDB and Cassandra are two such databases. You can have a look at NoSQL databases in this article.

Explore the top 10 SQL projects here.

Data Warehousing

Besides databases, it is also important for big data professionals to be proficient in working with data warehouses. Data warehouses are different from simple databases as they are specially designed to store data for analytical purposes.

Data stored in data warehouses is already aggregated from raw data and is ready to be consumed by data analysts. You will need to learn about data warehousing design architecture like Star and Snowflake schemas and when to use which one.

Hive is a very popular data warehouse tool. You can learn about Hive in this article.

Data Mining

For big data professionals, data mining is crucial. It enables the extraction of meaningful insights from vast datasets through techniques like association rule mining, classification, and clustering. By uncovering patterns and anomalies, data mining facilitates informed decision-making across industries like business, finance, and healthcare. Data mining enhances predictive modeling and trend forecasting, enabling professionals to derive actionable insights and drive strategic initiatives. It’s a cornerstone for unlocking the potential of big data, allowing professionals to harness its power for innovation and competitive advantage.

Explore these top 14 data mining projects.

Distributed Frameworks

Big data professionals or data architects work with distributed systems because big data cannot be stored and processed on a single machine. Single machines are prone to fail and can quickly overcome their storage.

There are a lot of distributed frameworks the most important of which is Apache Hadoop. Apache Hadoop has an ecosystem of tools serving various purposes. It has HDFS which is the distributed storage layer storing data in various worker nodes and handling fault tolerance. Then it has the MapReduce component responsible for the distributed processing of big data. Then it has components like Sqoop, HBase, etc. on top for handling the data. You can check out all of that over here.

Besides Hadoop, there is also Apache Spark which is more efficient than Hadoop in processing the data. It is much faster as it handles data in memory thereby reducing the input-output operations happening in MapReduce. You can read about Apache Spark in this article.

Cloud Computing

The task of a big data professional is essentially to design an architecture to manage and store big data. Now this architecture can be designed on-premise. However, given the low cost of resources on the cloud, a lot of organizations are moving their infrastructure to platforms like AWS, Azure, or GCP. Therefore, it is extremely important for big data professionals to understand the cloud platforms really well to effectively utilize the resources and design cost-effective pipelines.

Interpretation and Data Visualization

Effectively understanding and visually presenting data insights are vital for engaging stakeholders. Data visualization skills enable the development of relevant graphs, charts, and dashboards that aid in comprehension and decision-making. For this purpose, it is important to learn tools like Tableau or PowerBI. You can

Machine Learning

Besides learning big data technologies, big data engineers also need to learn about data science machine learning, and deep learning algorithms. This is important because the end user of any data warehouse or database is a data scientist. So if a big data engineer has good knowledge of machine learning algorithms, they will have a clear understanding of the data requirements from a data scientist or a data analyst. This will reduce the knowledge gap between the two and smooth out the automation process. So having knowledge of basic algorithms like linear regression, logistic regression, KNN, SVM, Neural Networks, CNNs, etc. will go a long way in a big data professional’s career.

Big Data Job Roles and Salary

Job RoleAverage Salary
Big Data Engineer₹ 3.6 Lakhs to ₹ 20.4 Lakhs
Data Engineer₹ 3.3 Lakhs to ₹ 20.9 Lakhs
Machine Learning Engineer₹ 3.0 Lakhs to ₹ 21.0 Lakhs
Big Data Architect₹ 14.7 Lakhs to ₹ 45.0 Lakhs
Data Analyst₹ 1.6 Lakhs to ₹ 12 Lakhs
Data Scientist₹ 3.6 Lakhs to ₹ 25.9 Lakhs
Data Governance Analyst₹ 3.7 Lakhs to ₹ 39.1 Lakhs
Data Warehouse Manager₹ 2.3 Lakhs to ₹ 13.3 Lakhs
Business Intelligence Developer₹ 3.0 Lakhs to ₹ 15.0 Lakhs
Data Visualization Specialist₹ 2.1 Lakhs to ₹ 17.0 Lakhs

Conclusion

The present era is witnessing a soaring demand for big data skills like programming languages, machine learning, distributed computing, and cloud computing. You may position yourself as a sought-after professional in the quickly changing field of big data by constantly updating your skill set and remaining adaptive. Achieving these 10 must-have big data developer skills or big data engineer skills described above will definitely raise your odds of landing a job in the big data area in 2024. Consider signing up for our Blackbelt+ program if you are interested in mastering big data skills!

Key Takeaways

  • Big data skills are essential for navigating today’s data-driven landscape.
  • Proficiency in problem-solving, programming languages, data structures, SQL/NoSQL databases, data warehousing, data mining, distributed frameworks, cloud computing, data interpretation, visualization, and machine learning is crucial.
  • These skills empower professionals to address complex data challenges and position them as invaluable assets in the evolving big data ecosystem, enhancing their career prospects in 2024 and beyond.

Frequently Asked Questions

Q1. Is big data a technical skill?

A. Big data is an area that necessitates a mixture of technical abilities such as programming, data administration, and data analysis and not a technical skill of its own.

Q2. Which big data skills are essential for working with big data?

A. Programming languages, quantitative analysis, data mining, data visualization, problem-solving, SQL/NoSQL databases, cloud computing, machine learning,  and continuous learning are all essential skills for big data.

Q3. What big data skills are most in-demand?

A. The big data skills currently high in demand are:
– Programming languages (Python, R, Java)
– Machine learning
– Data visualization
– Cloud computing (AWS, Azure)
– SQL /NoSQL databases

Q4. What are the 5 elements of big data?

A. Volume (large datasets), velocity (high-speed data generation), variety (different data kinds), veracity (uncertainty and noise in data), and value (extracting important insights from data) are the five elements of big data.

big data careersbig data skills
A
Analytics Vidhya

Analytics Vidhya Content team

Free Courses

Generative AI 4.7

Generative AI - A Way of Life

Explore Generative AI for beginners: create text and images, use top AI tools, learn practical skills, and ethics.
Large Language Models 4.5

Getting Started with Large Language Models

Master Large Language Models (LLMs) with this course, offering clear guidance in NLP and model training made simple.
Prompt Engineering 4.6

Building LLM Applications using Prompt Engineering

This free course guides you on building LLM apps, mastering prompt engineering, and developing chatbots with enterprise data.
LlamaIndex 4.8

Improving Real World RAG Systems: Key Challenges & Practical Solutions

Explore practical solutions, advanced retrieval strategies, and agentic RAG systems to improve context, relevance, and accuracy in AI-driven applications.
RAG systems using LlamaIndex 4.7

Microsoft Excel: Formulas & Functions

Master MS Excel for data analysis with key formulas, functions, and LookUp tools in this comprehensive course.

Recommended Articles

Responses From Readers

Write for us

Write, captivate, and earn accolades and rewards for your work
  • Reach a Global Audience
  • Get Expert Feedback
  • Build Your Brand & Audience
  • Cash In on Your Knowledge
  • Join a Thriving Community
  • Level Up Your Data Science Game
imag
Congratulations, You Did It!
Well Done on Completing Your Learning Journey. Stay curious and keep exploring!
9 Must-Have Skills to Become a Data Engineer!
9 Must-Have Skills to Become a Data Engineer!
14 Must Have Data Scientist Skills in 2024
14 Must Have Data Scientist Skills in 2024

We use cookies essential for this site to function well. Please click to help us improve its usefulness with additional cookies. Learn about our use of cookies in our Privacy Policy & Cookies Policy.

Show details