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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

How to Crack Data Science FAANG Jobs in 2025?

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Analytics Vidhya Last Updated : 05 Dec, 2024
6 min read

Data science has emerged as a highly sought-after field, and securing a job at a FAANG (Facebook, Amazon, Apple, Netflix, Google) company is considered a major achievement. FAANG companies are known for their innovative approaches, cutting-edge technologies, and attractive compensation packages. This article will discuss 15 tips that can help you crack data science FAANG jobs in 2024.

Table of contents

What is Special About FAANG Jobs?

Alt text: FAAMG companies

FAANG firms are well known for their clout, market dominance, and capacity to upend entire sectors of the economy. Working for a FAANG organization has numerous advantages, including access to enormous datasets, cutting-edge technology, collaborative work settings, and chances to work on ground-breaking projects. Additionally, the FAANG businesses frequently entice the top personnel from around the globe, creating a fiercely competitive labor market.

What Does FAANG Data Scientists Do?

Data scientists at FAANG (companies play a crucial role in leveraging data to drive business decisions, enhance user experiences, and develop cutting-edge technologies. Their responsibilities may include:

  1. Conducting in-depth analysis of large datasets to identify patterns, trends, and insights that inform strategic decisions and product improvements.
  2. Developing and implementing machine learning models and algorithms to solve complex problems like recommendation systems, fraud detection, and natural language processing.
  3. Creating visualizations and dashboards to present data-driven insights in a clear and actionable manner for stakeholders.
  4. Designing and analyzing A/B tests to evaluate the effectiveness of new features or product changes.
  5. Building predictive models to forecast user behavior, customer churn, or demand for products and services.
  6. Applying NLP techniques to process and understand unstructured text data for applications like sentiment analysis and chatbots.
  7. Collaborating with data engineers to develop and maintain data pipelines, ensuring efficient and reliable data flow.
  8. Staying up-to-date with the latest advancements in data science and exploring new methodologies to improve existing processes.
  9. Collaborating with product managers, engineers, designers, and other teams to understand business needs and deliver data-driven solutions.
  10. Ensuring data privacy and maintaining ethical standards when working with sensitive user data.

15 Tips to Get Your FAANG Jobs

Follow these tips to get a FAANG job:

#1. Build a strong foundation in data science and related concepts

To succeed in data science, it is essential to have a firm grasp of fundamental ideas in statistics, linear algebra, probability, and calculus. Develop a strong foundation in these areas to tackle complex data problems effectively.

#2. Master Python / R, programming languages commonly used in data science

The two most popular programming languages in the community of data scientists are Python and R. Mastering one or both of these languages will significantly enhance your chances of landing FAANG jobs.

#3. Become an expert in machine learning algorithms and techniques

Data science is built on machine learning. Learn about several machine learning algorithms, including clustering, regression, classification, and deep learning. Get practical experience applying these algorithms to datasets from the real world.

#4. Acquire knowledge of big data technologies and frameworks

FAANG companies deal with massive amounts of data. As a result, knowledge of big data technologies and frameworks like Apache, Hadoop, Spark, and Hive is crucial. Learn distributed computing principles and the effective handling and analysis of huge datasets.

FAANG Jobs
Source: Databricks

#5. Develop proficiency in data visualization and communication skills

Data scientists need to communicate their findings effectively to stakeholders. Use tools like Tableau, Power BI, or matplotlib to enhance your data visualization skills. 

Additionally, practice presenting complex ideas clearly and concisely to make an impact during interviews.

#6. Create an impressive portfolio of ds projects to showcase your skills

Create a portfolio of works showcasing your capacity to use data science methods to resolve real-world issues. Showcase your projects on platforms like GitHub or Kaggle to make them easily accessible to potential employers.

#7. Gain practical experience with real-world datasets

Practical experience is invaluable in the data science field. Seek opportunities to work on real-world datasets through internships, freelance projects, or research collaborations. This experience will give you an edge during the interview process.

#8. Contribute to open-source projects and participate in ds competitions

Participating in data science competitions and contributing to open-source projects are two ways to demonstrate your abilities and catch the eye of hiring managers. It exhibits your capacity for teamwork, problem-solving, and working on significant tasks.

#9. Utilize online platforms and communities to showcase your work

Leverage online platforms like Medium, Towards Data Science, or your blog to share your knowledge and insights. Engage with the data science community by joining forums, participating in discussions, and contributing valuable content.

#10. Understand the interview process and typical rounds

Each FAANG company’s interview process may include multiple rounds of technical assessments and behavioral interviews. Research and understand the interview structure for the specific company you are targeting. Practice solving technical interview questions and coding exercises to improve your problem-solving skills.

#11. Review and solidify your data science concepts, algorithms

Regularly review and revise data science concepts, algorithms, and statistical methods. Strengthen your understanding of machine learning, data preprocessing, feature engineering, and model evaluation techniques. This will help you tackle complex problems during interviews.

#12. Improve your coding skills and become familiar with ds libraries and tools

Coding skills are essential for data scientists. Concentrate on honing your programming skills by completing coding challenges and participating in data science projects. Learn about well-known frameworks and libraries like TensorFlow, Pandas, PyTorch, and NumPy.

#13. Enhance your communication, presentation skills for interview discussions

Data scientists often collaborate with cross-functional teams, including stakeholders from non-technical backgrounds. Improve your communication and presentation skills to convey your ideas and insights effectively during interviews.

#14. Build a strong professional network within the data science community

Networking plays a crucial role in landing  FAANG jobs. Attend conferences, meetups, and industry events to connect with professionals in the data science field. Join relevant LinkedIn groups and actively engage in discussions to expand your network.

#15. Leverage online platforms and industry events, and seek referrals

Use online resources like LinkedIn, GitHub, and Analytics Vidhya Community to promote your abilities and network with industry leaders. Attend industry gatherings, job fairs, and hackathons to network with possible employers. Additionally, seek referrals from professionals already working in FAANG companies, as employee referrals can significantly increase your chances of getting an interview.

FAANG Data Scientist Salaries

Data scientists get competitive salaries at FAANG which depend on factors like education skills, experience, location, and role. Check out the average salary for data science roles below:

OrganizationData Scientist Salary (in INR)Experience
Facebook69 LPA3 to 5 years
Amazon28 LPA 2 to 4 years
Apple 36 LPA0 to 9 years
Netflix 1 Cr5+ years
Google34 LPA1 to 10 years

Become a Data Scientist

Conclusion

Cracking a data science FAANG job requires technical skills, practical experience, and a strong professional network. Following the 15 tips outlined in this article can increase your chances of securing a data science job at a FAANG company in 2024. Remember to continuously learn and stay updated with the latest advancements in the field, as data science is a rapidly evolving discipline. If you want to learn the latest data science skills then explore our Blackbelt Plus program!

Frequently Asked Questions

Q1. Is data science still in demand in 2024?

A. In 2024, there will still be a big demand for data scientists. The demand for qualified data scientists will continue to be high due to the exponential growth of data and the requirement for data-driven decision-making.m

Q2. What is the career outlook for data science in 2024?

A. The career outlook for data science in 2024 is promising. With organizations increasingly relying on data to gain a competitive edge, the demand for data scientists is expected to grow. Skilled professionals who can extract insights from data and drive informed business decisions will have ample job opportunities.

Q3. How can I get a data science job at FAANG?

A. To secure a data science job at a FAANG company, it is crucial to develop a strong foundation in data science, master programming languages like Python or R, gain expertise in machine learning algorithms and big data technologies, and build a portfolio of impressive data science projects. Networking, participating in open-source projects, and preparing for technical interviews are essential.

Q4. Do FAANG companies hire data scientists?

A. Yes, FAANG companies hire data scientists to work on various aspects of their businesses, such as improving algorithms, personalization, recommendation systems, fraud detection, and data analytics. These companies heavily rely on data and require skilled data scientists to extract insights and drive innovation.

A
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