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Collection of Guides on Mastering SQL, Python, Data Cleaning, Data Wrangling, and Exploratory Data Analysis

KDnuggets

Are you curious about what it takes to become a professional data scientist? By following these guides, you can transform yourself into a skilled data scientist and unlock endless career opportunities. Look no further!

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Real Talk with A Data Scientist: The Future of Data Wrangling

Data Science 101

At Springboard , we recently sat down with Michael Beaumier, a data scientist at Google, to discuss his transition into the field, what the interview process is like, the future of data wrangling, and the advice he has for aspiring data professionals. in physics and now you’re a data scientist.

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Data Wrangling with Python

Mlearning.ai

Because it can swiftly and effectively handle data structures, carry out calculations, and apply algorithms, Python is the perfect language for handling data. Data wrangling requires that you first clean the data. It entails searching the data for missing values and assigning or imputed values to them.

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5 Reasons Why SQL is Still the Most Accessible Language for New Data Scientists

ODSC - Open Data Science

For budding data scientists and data analysts, there are mountains of information about why you should learn R over Python and the other way around. Though both are great to learn, what gets left out of the conversation is a simple yet powerful programming language that everyone in the data science world can agree on, SQL.

SQL 98
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Navigate your way to success – Top 10 data science careers to pursue in 2023

Data Science Dojo

Here, we outline the essential skills and qualifications that pave way for data science careers: Proficiency in Programming Languages – Mastery of programming languages such as Python, R, and SQL forms the foundation of a data scientist’s toolkit.

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The Top Ten Certifications For Data Analysts

Pickl AI

Tools and Techniques Commonly Used Data Analysts rely on various tools to streamline their work. Software like Microsoft Excel and SQL helps them manipulate and query data efficiently. They use data visualisation tools like Tableau and Power BI to create compelling reports. Data Science Certification Course by Pickl.AI

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State of Machine Learning Survey Results Part Two

ODSC - Open Data Science

First, there’s a need for preparing the data, aka data engineering basics. Machine learning practitioners are often working with data at the beginning and during the full stack of things, so they see a lot of workflow/pipeline development, data wrangling, and data preparation.