- Blog Categories
- Software Development Projects and Ideas
- 12 Computer Science Project Ideas
- 28 Beginner Software Projects
- Top 10 Engineering Project Ideas
- Top 10 Easy Final Year Projects
- Top 10 Mini Projects for Engineers
- 25 Best Django Project Ideas
- Top 20 MERN Stack Project Ideas
- Top 12 Real Time Projects
- Top 6 Major CSE Projects
- 12 Robotics Projects for All Levels
- Java Programming Concepts
- Abstract Class in Java and Methods
- Constructor Overloading in Java
- StringBuffer vs StringBuilder
- Java Identifiers: Syntax & Examples
- Types of Variables in Java Explained
- Composition in Java: Examples
- Append in Java: Implementation
- Loose Coupling vs Tight Coupling
- Integrity Constraints in DBMS
- Different Types of Operators Explained
- Career and Interview Preparation in IT
- Top 14 IT Courses for Jobs
- Top 20 Highest Paying Languages
- 23 Top CS Interview Q&A
- Best IT Jobs without Coding
- Software Engineer Salary in India
- 44 Agile Methodology Interview Q&A
- 10 Software Engineering Challenges
- Top 15 Tech's Daily Life Impact
- 10 Best Backends for React
- Cloud Computing Reference Models
- Web Development and Security
- Find Installed NPM Version
- Install Specific NPM Package Version
- Make API Calls in Angular
- Install Bootstrap in Angular
- Use Axios in React: Guide
- StrictMode in React: Usage
- 75 Cyber Security Research Topics
- Top 7 Languages for Ethical Hacking
- Top 20 Docker Commands
- Advantages of OOP
- Data Science Projects and Applications
- 42 Python Project Ideas for Beginners
- 13 Data Science Project Ideas
- 13 Data Structure Project Ideas
- 12 Real-World Python Applications
- Python Banking Project
- Data Science Course Eligibility
- Association Rule Mining Overview
- Cluster Analysis in Data Mining
- Classification in Data Mining
- KDD Process in Data Mining
- Data Structures and Algorithms
- Binary Tree Types Explained
- Binary Search Algorithm
- Sorting in Data Structure
- Binary Tree in Data Structure
- Binary Tree vs Binary Search Tree
- Recursion in Data Structure
- Data Structure Search Methods: Explained
- Binary Tree Interview Q&A
- Linear vs Binary Search
- Priority Queue Overview
- Python Programming and Tools
- Top 30 Python Pattern Programs
- List vs Tuple
- Python Free Online Course
- Method Overriding in Python
- Top 21 Python Developer Skills
- Reverse a Number in Python
- Switch Case Functions in Python
- Info Retrieval System Overview
- Reverse a Number in Python
- Real-World Python Applications
- Data Science Careers and Comparisons
- Data Analyst Salary in India
- Data Scientist Salary in India
- Free Excel Certification Course
- Actuary Salary in India
- Data Analyst Interview Guide
- Pandas Interview Guide
- Tableau Filters Explained
- Data Mining Techniques Overview
- Data Analytics Lifecycle Phases
- Data Science Vs Analytics Comparison
- Artificial Intelligence and Machine Learning Projects
- Exciting IoT Project Ideas
- 16 Exciting AI Project Ideas
- 45+ Interesting ML Project Ideas
- Exciting Deep Learning Projects
- 12 Intriguing Linear Regression Projects
- 13 Neural Network Projects
- 5 Exciting Image Processing Projects
- Top 8 Thrilling AWS Projects
- 12 Engaging AI Projects in Python
- NLP Projects for Beginners
- Concepts and Algorithms in AIML
- Basic CNN Architecture Explained
- 6 Types of Regression Models
- Data Preprocessing Steps
- Bagging vs Boosting in ML
- Multinomial Naive Bayes Overview
- Gini Index for Decision Trees
- Bayesian Network Example
- Bayes Theorem Guide
- Top 10 Dimensionality Reduction Techniques
- Neural Network Step-by-Step Guide
- Technical Guides and Comparisons
- Make a Chatbot in Python
- Compute Square Roots in Python
- Permutation vs Combination
- Image Segmentation Techniques
- Generative AI vs Traditional AI
- AI vs Human Intelligence
- Random Forest vs Decision Tree
- Neural Network Overview
- Perceptron Learning Algorithm
- Selection Sort Algorithm
- Career and Practical Applications in AIML
- AI Salary in India Overview
- Biological Neural Network Basics
- Top 10 AI Challenges
- Production System in AI
- Top 8 Raspberry Pi Alternatives
- Top 8 Open Source Projects
- 14 Raspberry Pi Project Ideas
- 15 MATLAB Project Ideas
- Top 10 Python NLP Libraries
- Naive Bayes Explained
- Digital Marketing Projects and Strategies
- 10 Best Digital Marketing Projects
- 17 Fun Social Media Projects
- Top 6 SEO Project Ideas
- Digital Marketing Case Studies
- Coca-Cola Marketing Strategy
- Nestle Marketing Strategy Analysis
- Zomato Marketing Strategy
- Monetize Instagram Guide
- Become a Successful Instagram Influencer
- 8 Best Lead Generation Techniques
- Digital Marketing Careers and Salaries
- Digital Marketing Salary in India
- Top 10 Highest Paying Marketing Jobs
- Highest Paying Digital Marketing Jobs
- SEO Salary in India
- Brand Manager Salary in India
- Content Writer Salary Guide
- Digital Marketing Executive Roles
- Career in Digital Marketing Guide
- Future of Digital Marketing
- MBA in Digital Marketing Overview
- Digital Marketing Techniques and Channels
- 9 Types of Digital Marketing Channels
- Top 10 Benefits of Marketing Branding
- 100 Best YouTube Channel Ideas
- YouTube Earnings in India
- 7 Reasons to Study Digital Marketing
- Top 10 Digital Marketing Objectives
- 10 Best Digital Marketing Blogs
- Top 5 Industries Using Digital Marketing
- Growth of Digital Marketing in India
- Top Career Options in Marketing
- Interview Preparation and Skills
- 73 Google Analytics Interview Q&A
- 56 Social Media Marketing Q&A
- 78 Google AdWords Interview Q&A
- Top 133 SEO Interview Q&A
- 27+ Digital Marketing Q&A
- Digital Marketing Free Course
- Top 9 Skills for PPC Analysts
- Movies with Successful Social Media Campaigns
- Marketing Communication Steps
- Top 10 Reasons to Be an Affiliate Marketer
- Career Options and Paths
- Top 25 Highest Paying Jobs India
- Top 25 Highest Paying Jobs World
- Top 10 Highest Paid Commerce Job
- Career Options After 12th Arts
- Top 7 Commerce Courses Without Maths
- Top 7 Career Options After PCB
- Best Career Options for Commerce
- Career Options After 12th CS
- Top 10 Career Options After 10th
- 8 Best Career Options After BA
- Projects and Academic Pursuits
- 17 Exciting Final Year Projects
- Top 12 Commerce Project Topics
- Top 13 BCA Project Ideas
- Career Options After 12th Science
- Top 15 CS Jobs in India
- 12 Best Career Options After M.Com
- 9 Best Career Options After B.Sc
- 7 Best Career Options After BCA
- 22 Best Career Options After MCA
- 16 Top Career Options After CE
- Courses and Certifications
- 10 Best Job-Oriented Courses
- Best Online Computer Courses
- Top 15 Trending Online Courses
- Top 19 High Salary Certificate Courses
- 21 Best Programming Courses for Jobs
- What is SGPA? Convert to CGPA
- GPA to Percentage Calculator
- Highest Salary Engineering Stream
- 15 Top Career Options After Engineering
- 6 Top Career Options After BBA
- Job Market and Interview Preparation
- Why Should You Be Hired: 5 Answers
- Top 10 Future Career Options
- Top 15 Highest Paid IT Jobs India
- 5 Common Guesstimate Interview Q&A
- Average CEO Salary: Top Paid CEOs
- Career Options in Political Science
- Top 15 Highest Paying Non-IT Jobs
- Cover Letter Examples for Jobs
- Top 5 Highest Paying Freelance Jobs
- Top 10 Highest Paying Companies India
- Career Options and Paths After MBA
- 20 Best Careers After B.Com
- Career Options After MBA Marketing
- Top 14 Careers After MBA In HR
- Top 10 Highest Paying HR Jobs India
- How to Become an Investment Banker
- Career Options After MBA - High Paying
- Scope of MBA in Operations Management
- Best MBA for Working Professionals India
- MBA After BA - Is It Right For You?
- Best Online MBA Courses India
- MBA Project Ideas and Topics
- 11 Exciting MBA HR Project Ideas
- Top 15 MBA Project Ideas
- 18 Exciting MBA Marketing Projects
- MBA Project Ideas: Consumer Behavior
- What is Brand Management?
- What is Holistic Marketing?
- What is Green Marketing?
- Intro to Organizational Behavior Model
- Tech Skills Every MBA Should Learn
- Most Demanding Short Term Courses MBA
- MBA Salary, Resume, and Skills
- MBA Salary in India
- HR Salary in India
- Investment Banker Salary India
- MBA Resume Samples
- Sample SOP for MBA
- Sample SOP for Internship
- 7 Ways MBA Helps Your Career
- Must-have Skills in Sales Career
- 8 Skills MBA Helps You Improve
- Top 20+ SAP FICO Interview Q&A
- MBA Specializations and Comparative Guides
- Why MBA After B.Tech? 5 Reasons
- How to Answer 'Why MBA After Engineering?'
- Why MBA in Finance
- MBA After BSc: 10 Reasons
- Which MBA Specialization to choose?
- Top 10 MBA Specializations
- MBA vs Masters: Which to Choose?
- Benefits of MBA After CA
- 5 Steps to Management Consultant
- 37 Must-Read HR Interview Q&A
- Fundamentals and Theories of Management
- What is Management? Objectives & Functions
- Nature and Scope of Management
- Decision Making in Management
- Management Process: Definition & Functions
- Importance of Management
- What are Motivation Theories?
- Tools of Financial Statement Analysis
- Negotiation Skills: Definition & Benefits
- Career Development in HRM
- Top 20 Must-Have HRM Policies
- Project and Supply Chain Management
- Top 20 Project Management Case Studies
- 10 Innovative Supply Chain Projects
- Latest Management Project Topics
- 10 Project Management Project Ideas
- 6 Types of Supply Chain Models
- Top 10 Advantages of SCM
- Top 10 Supply Chain Books
- What is Project Description?
- Top 10 Project Management Companies
- Best Project Management Courses Online
- Salaries and Career Paths in Management
- Project Manager Salary in India
- Average Product Manager Salary India
- Supply Chain Management Salary India
- Salary After BBA in India
- PGDM Salary in India
- Top 7 Career Options in Management
- CSPO Certification Cost
- Why Choose Product Management?
- Product Management in Pharma
- Product Design in Operations Management
- Industry-Specific Management and Case Studies
- Amazon Business Case Study
- Service Delivery Manager Job
- Product Management Examples
- Product Management in Automobiles
- Product Management in Banking
- Sample SOP for Business Management
- Video Game Design Components
- Top 5 Business Courses India
- Free Management Online Course
- SCM Interview Q&A
- Fundamentals and Types of Law
- Acceptance in Contract Law
- Offer in Contract Law
- 9 Types of Evidence
- Types of Law in India
- Introduction to Contract Law
- Negotiable Instrument Act
- Corporate Tax Basics
- Intellectual Property Law
- Workmen Compensation Explained
- Lawyer vs Advocate Difference
- Law Education and Courses
- LLM Subjects & Syllabus
- Corporate Law Subjects
- LLM Course Duration
- Top 10 Online LLM Courses
- Online LLM Degree
- Step-by-Step Guide to Studying Law
- Top 5 Law Books to Read
- Why Legal Studies?
- Pursuing a Career in Law
- How to Become Lawyer in India
- Career Options and Salaries in Law
- Career Options in Law India
- Corporate Lawyer Salary India
- How To Become a Corporate Lawyer
- Career in Law: Starting, Salary
- Career Opportunities: Corporate Law
- Business Lawyer: Role & Salary Info
- Average Lawyer Salary India
- Top Career Options for Lawyers
- Types of Lawyers in India
- Steps to Become SC Lawyer in India
- Tutorials
- C Tutorials
- Recursion in C: Fibonacci Series
- Checking String Palindromes in C
- Prime Number Program in C
- Implementing Square Root in C
- Matrix Multiplication in C
- Understanding Double Data Type
- Factorial of a Number in C
- Structure of a C Program
- Building a Calculator Program in C
- Compiling C Programs on Linux
- Java Tutorials
- Handling String Input in Java
- Determining Even and Odd Numbers
- Prime Number Checker
- Sorting a String
- User-Defined Exceptions
- Understanding the Thread Life Cycle
- Swapping Two Numbers
- Using Final Classes
- Area of a Triangle
- Skills
- Software Engineering
- JavaScript
- Data Structure
- React.js
- Core Java
- Node.js
- Blockchain
- SQL
- Full stack development
- Devops
- NFT
- BigData
- Cyber Security
- Cloud Computing
- Database Design with MySQL
- Cryptocurrency
- Python
- Digital Marketings
- Advertising
- Influencer Marketing
- Search Engine Optimization
- Performance Marketing
- Search Engine Marketing
- Email Marketing
- Content Marketing
- Social Media Marketing
- Display Advertising
- Marketing Analytics
- Web Analytics
- Affiliate Marketing
- MBA
- MBA in Finance
- MBA in HR
- MBA in Marketing
- MBA in Business Analytics
- MBA in Operations Management
- MBA in International Business
- MBA in Information Technology
- MBA in Healthcare Management
- MBA In General Management
- MBA in Agriculture
- MBA in Supply Chain Management
- MBA in Entrepreneurship
- MBA in Project Management
- Management Program
- Consumer Behaviour
- Supply Chain Management
- Financial Analytics
- Introduction to Fintech
- Introduction to HR Analytics
- Fundamentals of Communication
- Art of Effective Communication
- Introduction to Research Methodology
- Mastering Sales Technique
- Business Communication
- Fundamentals of Journalism
- Economics Masterclass
- Free Courses
Basic Fundamentals of Statistics for Data Science
Updated on 03 July, 2023
12.83K+ views
• 13 min read
Table of Contents
If you’re an aspiring Data Scientist, being familiar with the core concepts of Statistics for Data Science. You need not be a Ph.D. in Statistics to excel at Data Science, but you need to know enough to perhaps describe a couple of basic algorithms at a dinner party.
Going forward, we’ll walk you through some of the prerequisites in basics of Statistics for Data Science.
If you’ve just entered the world of Data Science, you might have come across people stating “Maths” as a prerequisite to Data Science. In all honesty, it’s not Maths, per se, but you have to learn Statistics for Data Science.
These days, libraries like Tensorflow hide almost all the complex Mathematics away from the user. Good for us, but it’s still good to have a basic understanding of the underlying principles on which these things work. Having a good understanding of data analytics can help you understand everything better.
This article will help arm you with some theorems, concepts, and equations that will not only help your cause as a Data Scientist but will also make you sound like you aced the course on Advanced Statistical Computing big time.
Basics of Statistics for Data Science
Statistical Significance: Differentiating between Random and Meaningful Results
Data analysis requires the ability to discern between random fluctuations in data and relevant patterns or consequences. This is where the statistical significance idea comes into play. Statistical significance assists academics and data scientists in determining whether observed results are due to chance or indicate a true link or impact.
Hypothesis testing, a commonly used statistical approach, is frequently used to determine statistical significance. The procedure begins with the formulation of a null hypothesis, which asserts that the data has no significant impact or link. In contrast, the alternative hypothesis implies the presence of a substantial impact or link.
Factual tests, such as the t-test or chi-square test, are used to determine quantitative relevance depending on the type of material and the review topic. If the faulty hypothesis is correct, these tests provide a p-value, which expresses the likelihood of achieving the specified outcomes by chance alone. A truly big p-value is one that is less than a defined limit, which is often 0.05.
It is critical to remember that factual significance does not always correspond to reasonable pertinence or the magnitude of the impact. Depending on the precise scenario and purposes of the assessment, a very crucial consequence may have little commonsense worth, although a non-significant finding may.
Understanding and using statistical significance is critical for obtaining trustworthy results from data analysis. Researchers may make educated judgments, support hypotheses, and discover significant results that contribute to the progress of knowledge and decision-making processes by distinguishing between random fluctuations and meaningful patterns.
Data Visualization: Communicating Insights through Graphs and Charts
Data visualization plays a crucial role in data analysis by transforming raw data into intuitive and visually appealing representations. Graphs and charts provide a powerful means of communicating insights, patterns, and trends hidden within complex datasets.
- Bar Charts: Bar charts are commonly used to display categorical data and compare values across different categories. They are effective in visualizing frequency counts, market shares, and other discrete data. Bar charts allow for quick comparisons and help identify the most significant categories or trends within the data.
- Line Charts: Line charts are ideal for visualizing trends and changes over time. They are commonly used to display time series data, such as stock prices or temperature fluctuations. Line charts allow data scientists to observe patterns, seasonality, and long-term trends, making them invaluable for forecasting and monitoring purposes.
- Scatter Plots: Scatter plots are useful for examining relationships between two continuous variables. By plotting data points on a Cartesian plane, data scientists can identify correlations, clusters, and outliers. Scatter plots help in understanding the nature of the relationship between variables and can aid in decision-making processes.
- Pie Charts: Pie charts are effective for illustrating the proportions or percentages of a whole. They are commonly used to display market shares, survey responses, or the distribution of categorical data. Pie charts provide a visual snapshot of relative proportions and make it easy to compare different categories at a glance.
- Heatmaps: Heatmaps are graphical representations of data where the values are encoded using colors. They are particularly useful for displaying large matrices or tabular data. Heatmaps help identify patterns, clusters, and relationships within datasets, making them valuable for tasks like correlation analysis and gene expression analysis.
Statistical Distributions
This is probably one of the most important things you need to know while arming yourself with prerequisite Statistics for Data Science.
Poisson Distribution
The Poisson distribution is one of the most essential tools in statistics. It’s used for to calculate the number of events that are likely to occur in a time interval. For instance, how many phone calls are likely to occur in any particular period of time.
The funny looking symbol in this equation (λ) is known as lambda. It is used to represent the average number of events occurring per time interval.Another good example where Poisson distribution finds use is to calculate the loss in manufacturing. Suppose a machine produces sheets of metal and has X flaws per yard. Suppose, for instance, the error rate was 2 per yard of the sheet – then using Poisson distribution, we can calculate the probability that exactly two errors will occur in a yard.
Binomial Distribution
If you’ve ever encountered basic Statistics, you might have come across Binomial Distribution.
Let’s say you had an experiment of flipping an unbiased coin thrice.
Can you tell the probability of the coin showing heads on all three flips?First, from basic combinatorics, we can find out that there are eight possible combinations of results when flipping a coin thrice. Now, we can plot the probabilities of having 0,1,2, or 3 heads. That plot will give us our required binomial distribution for this problem. When graphed, you’ll notice that it looks very similar to a typical normal distribution curve, in theory, both are very similar. While Binomial Distribution is for discrete values (a limited number of coin flips), Normal Distribution takes care of continuous values.
There are a number of distributions other than the ones we talked about above. If you’re an interested soul and also want to arm yourself better with the needed Statistics for Data Science, we suggest you to read up about the following distributions as well:
- Geometric Distribution
- Hypergeometric Distribution
- Discrete Uniform Distribution
- Negative Binomial Distribution
Exploratory Data Analysis and its Importance to Your Business
Top Data Science Skills to Learn to upskill
Some Theorems and Algorithms
When we talk about Statistics for Data Science, we just can’t ignore the basic theorems and algorithms that are the foundation of many libraries that you’ll be working on as a Data Scientist. There are a number of classification algorithms, clustering algorithms, neural network algorithms, decision trees, so on and so forth. In this section, we’ll talk about a few basic theorems that you should know – it’ll also help you understand other complex theorems with ease.
Bayes Theorem
This is one of the common theorems that you’ll come across if you’ve had any formal education in Computer Science. There have been numerous books over the years that excessively discuss Bayes Theorem and its concepts in an elaborate manner.
Bayes Theorem greatly simplifies complex concepts. It explains a lot of statistical facts using a few simple variables. It supports the concept of “conditional probability”(e.g., If A occurred, it played in role in the occurrence of B). The most appreciable thing about this is the fact that you can predict the probability of any hypothesis using just the given data points.
Bayes can help you predict the probability of someone having cancer just by knowing their age. It can also let you know if an email is spam based on the number of words. This theorem is in essence used to remove uncertainty.
Fun fact: Bayes Theorem helped predict locations of U-boats as well as predicting the configuration of the Enigma machine to translate the German codes, in WW2. Even in modern Data Science Bayes finds extensive applications in many algorithms.
The What’s What of Data Warehousing and Data Mining
K-Nearest Neighbor Algorithm
This is a very easy algorithm both in terms of understanding and implementation. So much so that it’s referred to as the “lazy algorithm”. Its simplicity lies in the fact that it’s based on logical deductions than any fundamental of statistics, per se. In layman terms, this algorithm looks to find groups closest to each other.
K-NN uses the concept of Euclidean Distance. It searches for local groups in and around a specified number of focal points. That number is represented by “k”. There are many approaches to finding out how large the value of ‘k’ should be as this is a user-decided value.
This concept is great for feature clustering, basic market segmentation, and seeking out outliers from a group of data entries. Most modern programming languages implement the K-NN algorithm in just two lines of code.
Explore our Popular Data Science Certifications
Bagging (Bootstrap aggregating)
Bagging essentially refers to creating more than one models of a single algorithm – like a decision tree. Each of the models is trained on a different sample data (this is called bootstrap sample).
Therefore, each decision tree is made using different sample data – this solves the problem of overfitting to the sample size. Grouping decision trees like this essentially help in reducing the total error, as the overall variance decreases with each new tree added. A bag of such decision trees is known as a random forest.
Get Started in Data Science with Python
ROC Curve Analysis
The term ROC stands for Receiver Operating Characteristic. The ROC analysis curve finds extensive use in Data Science. It predicts how well a test is likely to perform by measuring its overall sensitivity vs. its fall-out rate. ROC Analysis is extremely important when determining the viability of any model.
How does it work?
Your machine learning model might give you some inaccurate predictions. Some of them are because a particular value should’ve been ‘true’ but is instead set ‘false’, or vice-versa.
What is the probability of you being correct then?
Using the ROC curve, you can see how accurate your prediction is. With the two different parables, you can also figure out where to put your threshold value. The threshold is where you decide if the binary classification is positive or negative – true or false.
As the two parables get closer to each other, the area under the curve will tend to zero. This essentially means that your model is tending to inaccuracy. Greater the area, greater is the accuracy of your model. This is one of the first tests used when testing any modeling, as it helps detect problems early on by telling whether or not the model is correct.
A real-life example of ROC curves – They are used to depict the connection/trade-off between clinical sensitivity and specificity for cut-off for a particular test or a combination of tests – in a graphical way. To add to that, the area under the ROC curve also gives a fair idea of the benefits of using the tests mentioned above. Hence, ROC curves find extensive use in Biochemistry for choosing an appropriate cut-off. Ideally, the best cut-off is the one that has the lowest false positive rate with the highest true positive rate together.
How Can You Transition to Data Analytics?
upGrad’s Exclusive Data Science Webinar for you –
ODE Thought Leadership Presentation
Importance of Statistics in Data Science
From the above discussion now that you are aware of the basic concepts of Statistics and fundamentals of Statistics, let’s talk about the importance to learn Statistics for Data Science. The crucial tools and technologies to organize and find deep insights in the data, to analyze and quantify data are provided by Statistics for Data Analytics.
We have given you an overview of Statistics basic concepts and the impact of Statistics on data exploration, analysis, modelling, and representation. We also indicate to the problem if their is an inconsistency while neglecting the basics of Statistics. If you are interested in joining the fastest growing industry, come straight to our website at UpGrad to follow our Statistics for Data Science tutorial as we provide both online and offline courses in the same. Once you ace up your game in atleast the fundamentals of Statistics and the Basics of Statistics, you will job ready.
Read our popular Data Science Articles
In Conclusion…
The above list of topics is by no means a comprehensive list of everything you need to know in Statistics. This list is just to give you a flavor of what all you might encounter in your journey of Data Science, and how can you be prepared for it.
All in all, this article introduces to some of the core concepts of Statistics for Data Science. A deep understanding of the concepts explained coupled will help you understand the other concepts easily. If you would like to explore more and master data science, find our best online data science courses.
Frequently Asked Questions (FAQs)
1. What is the importance of Statistics for Data Science?
Statistics provides the techniques and tools for identifying structure in big data, as well as providing individuals and organisations with a greater understanding of the realities revealed by their data, using proper statistical methods which enables classification and organization, helps to calculate probability distribution and estimation, and find structure in data by spotting anomalies and trends. Statistics also helps in data visualisation and modeling with the use of graphs and networks. It aids in identifying data clusters or other structures that are affected by variables and helps to reduce the number of assumptions in a model, thereby making it more accurate and useful.
2. What are the key fundamental concepts of Statistics required for Data Science?
The core concepts of statistics are a must for data science. Here are some of the key concepts that help you get started on your data science journey:
1. Probability : This forms the basis for Data Science. Probability theory is quite useful in formulating predictions. Data is the foundation of all probability and statistics.
2. Sampling : Data sampling is a statistical analysis technique that involves selecting, manipulating, and analysing a representative selection of data points in order to find patterns and trends in a larger data collection.
3. Tendency and Distribution of Data : The distribution of data is a crucial factor. The significance of a well-known distribution such as the Normal Distribution is enormous. As a result, determining the distribution and skewness of data is a critical concept.
4. Hypotheses Testing : Hypotheses Testing identifies situations in which action should be done or not taken depending on the expected outcomes.
5. Variations : This refers to the distortion, error and shift in the data.
6. Regression : It is critical for Data Science since it aids in the understanding of existing solutions as well as the discovery of new innovations.
3. How is Statistics used in Data Science?
Data Scientists use statistics to help businesses make better product decisions, design and interpret trials, determining the factors that drive sales, forecast sales trends and patterns. Visual representation of data and algorithm performance helps find outliers, specific trivial patterns and metric summary.
Did you find this article helpful?
Our Trending Courses
MS in Data Science Post Graduate Programme in Data Science & AI (Executive) DBA in Emerging Technologies with concentration in Generative AIOur Trending Skill
Data Analysis Inferential Statistics Logistic Regression Linear Regression Linear Algebra for Analysis Hypothesis TestingGet Free Consultation
By clicking "Submit" you Agree toupGrad's Terms & Conditions