LearnWithMey
  • 👩‍💻Getting started
  • Introduction
    • What and Why Data Analytics?
    • Data Analyst Roadmap
    • Data Analysis Steps
    • Type of Data Analytics
    • Data Analytics Use Cases
    • Data Analytics Tools
    • Skills Requirement
    • Data Analyst vs Data Scientist
      • Key notes
  • STATISTICAL TECHNIQUES IN BUSINESS & ECONOMICS
    • Chapter 1: What is Statistics?
      • 1.1 What is meant by Statistics?
      • 1.2 Types of Statistics?
      • 1.3 Types of Variables
      • 1.4 Levels of Meaurement
      • Chapter Summary
    • Chapter 2: Describing Data
      • 2.1 Introduction
      • 2.2 Constructing a Frequency Table
      • 2.3 Constructing the Frequency Distributions
    • Chapter 3: Describing Data - Numerical Measures
      • 3.1 Introduction
      • 3.2 The population Mean
      • 3.3 The Sample Mean
      • 3.4 Properties of the Arithmetic Mean
      • 3.5 The Weighted Mean
      • 3.6 The Median
      • 3.7 The Mode
      • 3.8 The Geometric Mean
      • 3.9 Why Study Dispersion?
      • 3.10 Measures of Dispersion
      • 3.11 Interpretation and Uses of the Standard Deviation
      • 3.12 The Mean and Standard Deviation of Grouped Data
    • Chapter 4: Describing Data - Display and Explore Data
      • 4.1 Introduction
      • 4.2 Dot Plots
      • 4.3 Stem-and-Leaf Displays
      • 4.4 Measures of Position
      • 4.5 Skewness
      • 4.6 Describing the Relationship between Two Variables
    • Chapter 5: A Survey of Probability Concepts
    • Chapter 6: Discrete Probability Distribution
    • Chapter 7: Continuous Probability Distribution
    • Chapter 8: Sampling Methods and Central Limit Theorem
    • Chapter 9: Estimation and Confidence Intervals
      • 9.1 Introduction
    • Chapter 10: One-Sample Tests of Hypothesis
      • 10.1 Introduction
      • 10.2 Hypothesis Testing
    • Chapter 11: Two-Sample Tests of Hypothesis
    • Chapter 12: Analysis of Variance
    • Chapter 13: Correlation and Linear Regression
    • Chapter 14: Multiple Regression Analysis
    • Chapter 15: Index Number
    • Chapter 16: Time Series and Forcasting
    • Chapter 17: Nonparametric Methods: Goodness-of-Fit Tests
    • Chapter 18: Nonparametric Methods: Analysis of Ranked Data
    • Chapter 19: Statistical Process Control and Quality Management
    • Chapter 20: Decision Theory
    • Tables ( Z and Student's T )
  • Python for Data Science
    • Handbook
    • Introduction
    • Installation
    • Basic Syntax
    • 2. Introduction to Numpy
  • Excel for Data Analysis
    • Introduction
  • Data Warehousing
    • Introduction
    • Data Warehouse Architecture
  • Data Visualization with Power BI
    • Introduction
  • Data Analysis with Python
    • Importing Datasets
      • Understanding Data
      • Python Packages for Data Sciences
    • Data Wrangling
      • Pre-processing Data in Python
      • Dealing with Missing Values in Python
      • Data Formatting in Python
  • Machine Learning
    • Page
    • 1. Introduction
    • Machine Learning Exercises
  • React
    • Lazy Loading
  • Search Tips
  • Secure Product Design
  • Google like a pro
  • Project Management
  • SEO in React.js
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  1. Introduction

Skills Requirement

Statistics is most required skills for every data analyst

Let see the reason why you should explore about skill requirement before getting into Data Analytics or Data Science field is Statistics:

Studying statistics offers numerous benefits and is valuable in a variety of fields. Here are some reasons why studying statistics is important:

  1. Data Analysis: Statistics provides the necessary tools and techniques for analyzing and interpreting data. It equips individuals with the ability to collect, organize, summarize, and analyze data, enabling them to draw meaningful conclusions and make informed decisions based on evidence.

  2. Decision-making: Statistics plays a crucial role in decision-making processes. It helps individuals and organizations make informed choices by providing quantitative evidence and insights. Statistical analysis allows for the evaluation of risks, the comparison of options, and the identification of trends and patterns that can guide decision-making.

  3. Research and Scientific Studies: Statistics is fundamental to conducting research and scientific studies. It provides the methods and techniques to design experiments, collect data, analyze results, and draw valid conclusions. Statistical knowledge is essential for researchers in various fields, including medicine, social sciences, economics, and environmental studies.

  4. Data-driven Insights: In today's data-driven world, the ability to extract insights from large volumes of data is highly valued. Statistics enables individuals to identify trends, patterns, and relationships within datasets, providing valuable insights for businesses, organizations, and policymakers.

  5. Probability and Uncertainty: Statistics is closely related to probability theory, which deals with uncertainty and random events. Understanding probability allows individuals to assess the likelihood of different outcomes and make informed decisions in situations with uncertain outcomes. It is particularly relevant in fields such as finance, insurance, and risk management.

  6. Critical Thinking and Problem-solving: Studying statistics enhances critical thinking and problem-solving skills. It involves analyzing complex problems, formulating hypotheses, designing experiments, and applying statistical methods to find solutions. Statistics helps individuals develop a logical and systematic approach to problem-solving and fosters a deeper understanding of data and its implications.

  7. Career Opportunities: Proficiency in statistics opens up a wide range of career opportunities. Many industries, including finance, marketing, healthcare, technology, and government, require professionals with strong statistical skills. Data analysts, statisticians, market researchers, and data scientists are in high demand, and studying statistics can provide a competitive edge in these fields.

  8. Understanding Research Literature: Statistics is essential for understanding and critically evaluating research literature. Whether reading scientific papers, market reports, or policy briefs, having a solid understanding of statistical concepts allows individuals to assess the reliability of findings, identify potential biases, and make informed judgments about the validity of research.

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Last updated 1 year ago