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Reading list

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
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 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 Become a Data Scientist at Google?

a
avcontentteam 21 Feb, 2024
6 min read

Are you eager to work for a multinational company but nervous about the job interview? Don’t let fear hold you back. Embrace the selection process as an opportunity for growth and potential employment. Landing a Data Scientist role at Google requires understanding their needs, aligning with their criteria, showcasing your expertise and standout qualities. With dedication and preparation, you can make your dream job a reality. Discover the requirements and begin your journey toward realizing your aspirations!

Table of contents

Role of a Data Scientist at Google

  • Work under or assist General Manager and other interdisciplinary teams in the identification of opportunities and addressing users in the region
  • Handle the large data sets
  • Work on non-routine investigative issues
  • Must be able to provide quantitative support, advanced and different perception-loaded strategic planning and good market understanding to the partner companies of Google
  • Provide compelling, interesting and novel recommendations with good presentation and communication skills to exhibit all the levels of stakeholders.

Job Description of Data Science Jobs at Google

  • Develop predictive models through the use of advanced and updated forms of Machine learning algorithms
  • Provide solutions to business problems based on data 
  • Interact with cross-functional teams for better efficiency of project and problem solving 
  • Work on large datasets, recognize the trends and interpret the same for decision making 
  • Effectively and efficiently use Google Cloud Platform (GCP) tools such as Dataproc, BigQuery, Dataflow and Pub/Sub. 
  • Work near software engineers to compile data science knowledge into products 
  • Be updated with industry trends and best practices 
  • Lead or mentor the junior team members and support them with technical issues.

Google Cloud Platform | Data Scientist at Google

Also Read: Explore Data Science Salaries in India and Future Trend

What is Google looking for in a Data Scientist?

Google is particular in its wants; hence, the prime need is job-based skills and people who can work in the office environment. If you need a certain skill, work on building them. Though it’s a time taking process, it is certainly helpful in the long run. 

Hard Skills

  • Preferred education qualification – a Master’s degree or PhD (as specified in the job role) in Computer Science, Statistics or other relevant fields
  • Relevant work experience 
  • Expertise in at least a database language such as SQL
  • Expertise in at least one statistical language, such as R or Python
  • Ability to work on Natural Language Processing, Hadoop, SPARK and statistics
  • Familiarity with Machine Learning and its algorithms
  • Excellent presentation skills through Power BI, Excel and Tableau
  • Other job-specific achievements worth sharing.

Soft Skills

  • Ability to communicate with other experts from different fields, such as Product Management, Engineering or tech teams
  • Have the skill to connect the dots and look for the overview of the task you are working on. Ability to gain a broad view of the problem. 
  • Communicate effectively with the teams, clients, stakeholder teams and customers.

General Skills

  • Collaboration with team members is essential for compatibility and success in any work or project
  • Teamwork combines diverse skill sets and expertise required for specific tasks
  • Share experiences and contributions in group projects, including challenges and how you overcame them
  • Demonstrate resilience by starting afresh after setbacks
  • Express your mindset and provide real-life examples that illustrate your determination
  • Communicate your career aspirations, goals, and passion for the job
  • Emphasize that you are driven by more than just a paycheck
  • Highlight your integrity and reliability to give employers a reason to trust and hire you. 

Python Libraries for Data Science
Source: Our Code World

Interview Process for Data Scientist at Google

Expressing yourself rightly during the interview is the most required thing. The process includes:

1. Initial Screen

A 30-minute phone interview with a recruiter wherein you discuss the job and the work-life at Google. The recruiter will pose questions to learn about your professional experiences, skills, and career goals to understand if you are the right fit for Google’s culture.

2. Technical Screen

This video-based round with a Google data scientist focuses on experimental design, statistics, and a probabilistic coding question.

The interviewer may also discuss your past research and work experience as well as your approach to solving the question

3. Onsite Interview

This part includes 5 one-on-one rounds with data scientists, each lasting about 45 minutes. . These rounds cover computational statistics, product interpretation, probability, metrics and experimentation, modeling, and behavioral questions.

Google understands employees’ hidden nervousness and anxiety and provides guidelines to relax the issues specifically for Google data science jobs. 

  • They want you to tell them about your passion, your choice, your interests and why you enjoy the field you want to enjoy. Telling something you love is the best way to relax, and they help you out. 
  • Every employer requires teamwork. Google does so and wants you to understand the same. They check whether you are aware of the environment and have any strategy or method to carry out the activities at the office. Learn about the experience at their branches to have a good chance. 
  • The more personalized invitation letter or email intrigues you. Similarly, your customized resume will intrigue them as well. Ensure to make all the necessary changes keeping the company and job role in mind to present it as it’s curated for them solely. 
  • The inability to crack an interview isn’t the end of the world. Instead, you are way ahead of the first time. If you analyze rightly, you are more aware of what they need. Try again with an improved strategy and self-improvement. 
  • Timely completion of assessments with high-quality showcases and speaks for you. Give your best and be yourself. They will assess your knowledge, analytical skills and other job-related skills. Ensure to exhibit all of it. 

How to Apply for?

  1. Go to the careers page on the official Google website (https://careers.google.com/). 
  2. Click on the ‘jobs’ available at the top of the page. 
  3. Specify the job title. ‘Data scientist’ in this case of finding Google data science jobs. Choose your preferred locations and other further options to filter out the results. 
  4. Check the list of jobs. Read the job description, eligibility criteria, expectations and other associated details. 
  5. Click the ‘Apply’ button to get Google data scientist jobs. 

Perks and Benefits : Data Scientist at Google

You can expect different work benefits at the tech giant while working on Google data scientist jobs, such as:

  • Personal and professional development support
  • Full health insurance includes physical therapies, massage services, and on-site access to physicians and chiropractors. 
  • Fertility assistance
  • 18-22 weeks of maternity leave
  • Charitable giving matching 
  • 401 (K) matching
  • Adoption assistance
  • Dog-friendly offices 
  • Complimentary meals
  • On-site fitness centers and health classes

Wrapping Up

The future outlook for Google Data Scientists is highly promising. With the increasing reliance on data-driven decision making in various industries, the demand for skilled data scientists is expected to continue growing. Google, being a technology giant, is likely to invest heavily in data science and analytics to drive innovation and enhance its products and services. As data becomes more valuable, the role of data scientists at Google will become even more critical, offering excellent career opportunities and growth potential in the field.

Here are a few articles that can help with your Google data scientist interview:

If you dont have the necessary skills to become a data scientist at google, its time to buckle up and take charge of your career. Join our Blacklet Plus program and master all the essential data science skills.

Frequently Asked Questions

Q1. Can a data scientist work in Google?

A. Yes, data scientists can work at Google. Google employs many data scientists across various teams and departments to analyze data, develop algorithms, and extract insights to improve products, services, and decision-making processes.

Q2. What is the salary of data scientist in Google?

A. The salary of a data scientist at Google can vary depending on factors such as experience, location, and level of seniority. On average, Google offers competitive salaries to data scientists, with entry-level positions starting around $120,000 yearly and higher levels reaching well over $200,000 annually.

Q3. Is it hard to be a data scientist at Google?

a. Becoming a data scientist at Google can be challenging due to the high standards and rigorous hiring process. Google typically looks for candidates with strong technical skills, advanced data analysis and machine learning knowledge, and the ability to solve complex problems using data-driven approaches.

Q4. What is the role of data scientist in Google?

A. The role of a data scientist at Google involves leveraging data to drive insights, make informed decisions, and improve various aspects of the company’s operations. They work on data analysis, statistical modeling, developing machine learning algorithms, creating data visualizations, and collaborating with cross-functional teams to solve complex business problems.

data scienceData Scientist at GoogleGoogle Data Science SalaryGoogle Data Scienctist
a
avcontentteam 21 Feb, 2024

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