Are you a wannabe Data Scientist with a passion for data and Stats? Do you want to learn the tools and techniques in every Data Scientist’s arsenal?
Then you have come to the right place, as Machine Learning and Data Architecture alone cannot help you in your Data Science tasks.
You must also master the key concepts of Statistics like Regression Analysis to model the insights and trends.
So what are you waiting for? Check out this article to understand Data Science fundamentals and Regression techniques.
Register at a Data Science Bootcamp and launch your career in this data-driven space for lucrative and challenging opportunities.
An Introduction to Data Science
As the term suggests, “Data Science” is the science of using data to solve problems.
It combines various disciplines like Maths, Statistics, Machine Learning, Computer Science and Programming to extract insights from data.
Various tools and knowledge of databases are leveraged to organize the data for understanding patterns
Data Science is about gathering data, crunching data, extracting patterns and anomalies, and using them for analysis, predictions, and decision-making.
It pulls out the information hidden within the data and translates the data into a story.
It finds applications in every scenario where data exists, and a problem needs solving. Thus most industries, like Finance, Healthcare, Logistics, Transportation, E-commerce, Consulting, Agriculture, Education, and Robotics, are using Data Science to solve business challenges, roll out improved products and services, and serve their customers better.
The business or organization uses Data Science to solve their problems and bring innovation to their business models, reap efficiencies in their business processes, manage costs, increase marketing ROI, identify new business opportunities, and tap market opportunities.
Some real-world applications of Data Science are self-driving cars, movie recommendations on Netflix, obtaining relevant results on search engines, using a chatbot for customer support,
The Data Science lifecycle typically walks through the following:
- Defining the problem statement, i.e., asking the right questions.
- Collecting the data (structured, unstructured) from weblogs, social media, customer behavior, transactions, etc.
- Transforming the data into usable/standardized format and entering it into the system
- Data warehousing and Data Architecture
- Data Cleaning
- Normalizing the data
- Data Processing
- Data exploration – data mining, data classification, data clustering, data modeling
- Discovering patterns and trends
- Data Analyzing – regression, qualitative analysis, text mining, predictive analysis
- Making predictions
- Communicating insights – Presenting the conclusions and results in a way the stakeholders understand, including data visualization, reporting
Besides the above, Data Science also involves digging into the toolbox to visualize, program, formulate, develop and deploy hypotheses models that result in robust predictions.
It calls for expertise in the use of the right techniques and tools to mine data, build relevant models and predict outcomes.
Data Science and Statistics
Data Science works with Big Data, more than often, raw and streaming data. Much of the data exists as Big Data, in unstructured format and non-numeric.
It means the raw data has to be processed to cut out the noise for the extraction of insights.
Throughout the Data Science lifecycle, Statistical principles like Descriptive and Inferential Statistics are used to gather data, architect the data, and analyze and interpret the results.
Data Scientists use Statistical rules and methods to research and validate the results for inference and predictions.
Various Statistical tools/software like R, SPSS, MATLAB, Stata, SAS, Excel, GraphPad Prism, and Minitab are available to the Data Scientist for cutting-edge analysis. These are tools that assist in collecting and analyzing data for scientific insights into trends and patterns.
The tools use Statistical theorems of probability and various methods to perform Data Science, such as Regression and Time Series Analysis.
The key Statistical Techniques in use are Linear Regression, Resampling, Classification, Shrinkage, Subset Selection, Dimension Reduction, Unsupervised Learning, Tree-based Methods, and Support Vector Machines.
Regression as a Statistical Technique
Regression Analysis is a Statistical method to explore relationships between one dependent (criterion) variable and two or more independent (predictor) variables.
It explores the strength of the relationship and models future relationships using mean, median, and normal distributions.
In other words, Regression is a method used to establish which factors are most important for the problem, which variables to ignore (the outliers), and how they impact each other.
It discovers patterns in the data by analyzing the relationship between variables and makes a “best guess” to make a prediction.
This is done by identifying the curve or line that best fits the variables. Thus, Regression is best used for trend analysis and predictions.
How is Regression tied to Data Science?
Regression in data Science analysis is used for prediction, forecasting, and inferring causal relationships between independent and dependent variables.
There are various types of regressions used in data science and machine learning. Each type has its importance in different scenarios and is selected for the best way it can solve the problem.
The important types of Regression used in Data Science are:
- Linear Regression
- Polynomial Regression
- Logistic Regression
- Decision Tree Regression
- Random Forest Regression
- Support Vector Regression
- Ridge Regression
- Lasso Regression
The type of Regression technique used depends upon the existing variables and the outcomes required.
Regression is also critical for any Machine Learning problem that crunches continuous numbers.
Why do organizations use Regression Analysis?
The advantages of using Regression are predictive analytics and optimization operations to understand what variables are significant and what to disregard.
It cuts down guesswork and hypotheses in decision-making by executing a scientific way of analyzing data and predicting outcomes.
Regression Analysis is widely used in Finance for use cases like forecasting revenues and expenses, in Marketing campaigns to determine target customer groups, in Logistics and Supply Chains for predicting inventory levels, in Market Research and Sales as a forecasting tool, and so on.
Data Science is a domain that involves knowledge of data structures, data warehousing and architecture, common programming techniques like Python and C++, and Machine Learning and Statistical methods.
Even where businesses are automated and companies use sophisticated software, knowledge of Statistics, particularly Regression in Data Science Analysis, is key to Data Science implementation.
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