- Statistics and Probability Knowledge Checklist
- Top 18 Probability and Statistics Interview Questions for Data Scientists
- What Is the Difference Between Descriptive and Inferential Statistics?
- What Are the Main Measures Used to Describe the Central Tendency of Data?
- What Are the Main Measures Used to Describe the Variability of Data?
- What Are Skewness and Kurtosis?
- Describe the Difference Between Correlation and Autocorrelation
- Explain the Difference Between Probability Distribution and Sampling Distribution
- What Is the Normal Distribution and How Is It Characterized?
- What Are the Assumptions of Linear Regression?
- What Is Hypothesis Testing?
- What Are the Most Common Statistical Tests Used?
- What Is the P-Value and How Can We Interpret It?
- What Is the Confidence Interval?
- What Are the Main Ideas of the Law of Large Numbers?
- What Is the Central Limit Theorem?
- What's the Difference Between Population and Sample in Data Analysis?
- The Difference Between Probability and Likelihood
- What's Your Knowledge of Statistics, and How Have You Used it as a Data Analyst?
- How to Data Scientists Use Statistics?
- General Interview Tips & Tricks
- Probability and Statistics Interview Questions for Data Scientists: Next Steps
Top 10 Data Analytics Tools You Need To Know
Top 10 Data Analytics Tools You Need To Know The word ‘Data’ has been in existence for ages now. In the era of 2.5 Quintillion bytes of data being generated daily, data plays a crucial role in decision-making for business operations. But how do you think we can deal with so much data? Well, there are several roles in the industry today that deal with data to gather insights, and one such vital role is of a Data Analyst. A Data Analyst requires many tools to gather insights from data. This article on the T op 10 Data Analytics Tools will discuss the top tools that every budding Data Analyst to a skilled professional must learn in 2023. In this article, we will explore a range of popular data analysis tools, including R and Python, Microsoft Excel, Tableau, RapidMiner, KNIME, Power BI, Apache Spark, QlikView, Talend, and Splunk. These tools offer diverse capabilities for data analysts and are widely used in the industry for various analytical tasks. Let’s div...
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