This presentation provides an overview of the steps and requirements to become a data scientist at Google. It covers the skills and qualifications needed, the application process, and tips for success. Whether you're a recent graduate or a seasoned professional, this presentation offers valuable insights and advice for anyone interested in pursuing a career as a data scientist at Google. This presentation provides an overview of the steps and requirements to become a data scientist at Google. It covers the skills and qualifications needed, the application process, and tips for success. Whether you're a recent graduate or a seasoned professional, this presentation offers valuable insights and advice for anyone interested in pursuing a career as a data scientist at Google.In this competitive field, it's essential to showcase your expertise in data analysis, machine learning, and programming languages such as Python and R. Additionally, having a strong background in statistics and experience with big data tools like Hadoop and Spark can give you a competitive edge in the application process. Let's dive deeper into the key steps and requirements for becoming a data scientist at Google.

1. Becoming a Data Scientist at Google
This presentation provides an overview of the skills required to become a data scientist at Google.

2. 01
02
Data Cleaning and Wrangling
Handle missing values, remove duplicates, and format
data
Transform raw data into a format suitable for analysis

3. ● Use algorithms and statistical models for
predictions and decisions
● Apply machine learning techniques to analyze
data
Machine Learning

4. 01
02
Data Visualization
Use data visualization tools like Matplotlib or Tableau
Create charts and graphics to enhance data
understanding

5. 01
02
SQL and Database
Management
Write efficient SQL queries to manipulate and analyze
data
Work with SQL databases for data storage and
management

6. 01
02
Big Data Processing and
Cloud Computing
Utilize cloud-based platforms (AWS, Azure, Google
Cloud) for data storage and processing
Process large datasets using technologies like Hadoop
and Spark

7. Data Modeling and Management
● Create mathematical models to represent data
● Manage data to ensure quality, accuracy, and
usefulness

8. Thank you for your time and attention 🙂