Remove EDA Remove Hypothesis Testing Remove SQL
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Data Analysis vs. Data Visualization – More Than Just Pretty Charts

Pickl AI

Exploratory Data Analysis (EDA): Using statistical summaries and initial visualisations (yes, visualisation plays a role within analysis!) Hypothesis Testing: Formally testing assumptions or theories about the data using statistical methods to determine if observed patterns are statistically significant or likely due to chance.

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Roadmap to Learn Data Science for Beginners and Freshers in 2023

Becoming Human

One is a scripting language such as Python, and the other is a Query language like SQL (Structured Query Language) for SQL Databases. There is one Query language known as SQL (Structured Query Language), which works for a type of database. SQL Databases are MySQL , PostgreSQL , MariaDB , etc.

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The Data Dilemma: Exploring the Key Differences Between Data Science and Data Engineering

Pickl AI

With expertise in programming languages like Python , Java , SQL, and knowledge of big data technologies like Hadoop and Spark, data engineers optimize pipelines for data scientists and analysts to access valuable insights efficiently. Statistical Analysis: Hypothesis testing, probability, regression analysis, etc.

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Top 10 Data Science Interviews Questions and Expert Answers

Pickl AI

Here are some key areas often assessed: Programming Proficiency Candidates are often tested on their proficiency in languages such as Python, R, and SQL, with a focus on data manipulation, analysis, and visualization. However, there are a few fundamental principles that remain the same throughout.

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Basic Data Science Terms Every Data Analyst Should Know

Pickl AI

Exploratory Data Analysis (EDA): Analysing and visualising data to discover patterns, identify anomalies, and test hypotheses. Inferential Statistics: A branch of statistics that makes inferences about a population based on a sample, allowing for hypothesis testing and confidence intervals.