Remove Big Data Remove EDA Remove Hypothesis Testing
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How To Learn Python For Data Science?

Pickl AI

Statistics Understand descriptive statistics (mean, median, mode) and inferential statistics (hypothesis testing, confidence intervals). These concepts help you analyse and interpret data effectively. Perform exploratory Data Analysis (EDA) using Pandas and visualise your findings with Matplotlib or Seaborn.

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From Data to Decisions: Deep Dive into Workshop Learnings

Women in Big Data

Hypothesis Testing in Action: We learned how to formulate a null hypothesis (no difference exists) and an alternative hypothesis (a difference exists) and use statistical tests to evaluate their validity. EDA involves techniques like: Identifying different types of variables (categorical, numerical).

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

Pickl AI

They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. 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.

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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. What is the Central Limit Theorem, and why is it important in statistics?

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Basic Data Science Terms Every Data Analyst Should Know

Pickl AI

B Big Data : Large datasets characterised by high volume, velocity, variety, and veracity, requiring specialised techniques and technologies for analysis. Deep Learning : A subset of Machine Learning that uses Artificial Neural Networks with multiple hidden layers to learn from complex, high-dimensional data.