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Data Science

What Is Data Science?

What Is Data Science? The term has been tossed around a lot lately, from newspapers and companies to countries. Whether you are looking for a new job or just curious about the latest technology, data science has impacted many aspects of our society. It has the potential to transform the way we do business. Read on to learn more about the process and find out what makes data science so exciting. Listed below are a few of its most common applications.

Law enforcement uses data science for a variety of purposes. The Belgian police, for instance, use it to better understand crime prevention. Their limited resources mean they can’t cover an extensive area, so using data science can help them anticipate criminal activity before it occurs. Likewise, logistics companies can use data science to optimize routes and detect fraudulent transactions. And of course, many other industries rely on data science for a variety of reasons.

The first phase of data science is data discovery. Data discovery can take many forms, from structured to unstructured. Today, many organizations are peeping into the social media accounts of their customers to understand their mindsets. Once organizations have an idea of the types of data they want to analyze, they collect it. As they gain clarity, they can create models based on that data. Data science can be applied to any type of business and will ultimately change our lives.

Another example is Lunewave, a company that uses data analytics to improve sensors. The company uses machine learning to create better sensors. The state’s main goal is to reopen schools, but it was also cautious due to the ongoing COVID-19 pandemic. Data science helped them streamline investigations, expedite contact tracing, and coordinate prevention measures. And while this case is extreme, it is not the only example of data science in action.

The data scientists are responsible for the creation of predictive models and algorithms. They must select the right algorithms to build a model for each problem and stay focused on the problem at hand. Once they’ve created a model, they need to share their findings with stakeholders, but it’s an underrated skill in the industry. It’s not always the data that drives the results. Data science can also help companies identify their target audiences and make targeted ads that are more effective.

Data scientists are responsible for gathering, organizing, and interpreting large amounts of data and transforming it into actionable information. The data scientists analyze the data, create analytical models, and communicate the findings to stakeholders. This process is crucial to the success of any company, but without effective data science, you can end up hurting your business by making the wrong decisions. So, what is Data Science? You can use it to improve your business and your customer’s life!

The benefits of data science are vast. For example, companies can analyze large amounts of data and build predictive models to reduce the likelihood of fraudulent transactions. Similarly, they can use data science to make accurate guesses about what the future holds. Facebook, for example, can identify people in pictures with surprising accuracy thanks to image recognition. It can even help predict which customers will buy products, thereby improving sales and operational efficiency. All of this can give you a competitive edge over your competitors.

How to Study Data Science

If you’re interested in pursuing a career in data science, there are several things you can do to prepare yourself. Getting hands-on experience in the field is important. Volunteering, hackathons, and positions at closely related companies are all great ways to get some experience. These activities can help expose you to the realities of working in tech and help you land a job. The following are some tips on how to study data science.

Understand the problem faced by your stakeholder. Using statistics or machine-learning techniques, approach the problem from their point of view. Remember that most business owners want to know the impact that data analysis has on their bottom line, not just how the data is used to solve their problems. You should be focusing on providing value to your company and building lasting relationships with your stakeholders. For example, you’ll need to work with product managers, marketers, designers, and software developers, among others. Ultimately, you’ll need to work with your entire workforce.

As a business, data science is becoming increasingly important. It helps you identify inefficiencies in manufacturing processes and predict future trends. Data science helps businesses understand their customers and their needs, and it allows them to adapt their marketing and product plans accordingly. Businesses will be able to meet their customers’ expectations with more accurate and tailored products. These benefits will ultimately make your business more profitable. So, why not consider a career in data science? There are countless benefits.

Firstly, you should have a solid foundation in mathematics. It is vital to be able to think critically and communicate effectively. In data science, you should study linear algebra and classical statistics, as they are the foundation for many machine learning algorithms. Learning linear algebra is crucial as well because it provides the basis for many inferential techniques. In addition, you should learn how to apply the knowledge you gain in these subjects in the context of the business you’re working with.

The Prolific Automation Course Data Science

If you are looking for an industrial training institute that specializes in providing training for the automation industry, you may want to consider the Prolific Automation Course Data Science. The program provides training on a wide range of topics in the industrial field and NSDC-approved training courses. Students will have the opportunity to work on actual PLCs and DCS to get hands-on experience in real-world applications. In addition to training, students will be able to find employment through placement programs.

This online course covers a variety of data structures. Students will learn how to construct lists, tuples, and dictionaries, as well as how to perform operations on data. It also features 6 chapters with multiple lectures, activities, and 3 problem sets. During the last two weeks of the course, students must complete a midterm exam to demonstrate their knowledge of data structures. The course is available at no additional cost and requires approximately two to four hours per week to complete.

In order to conduct programmatic follow-up studies, researchers can use the Prolific API. These programs can unlock the power to scale. Through these partnerships, researchers can find high-quality participants easily. Researchers still create an account, pay participants, and handle some of the processing through a partner tool. The Prolific API also enables researchers to conduct programmatic bonuses. The API allows researchers to easily find high-quality participants. The researchers still create their own account and pay participants, but the partner tool handles some of the more complicated steps.

Students can learn the ins and outs of Python and machine learning. They will learn about variables, expressions, and conditional statements, and the role of functions in the programming language. They will also learn about variable scoping, loops, and iterations, as well as how to create data structures. This training will prepare students to be productive, intelligent, and capable data scientists and analysts. And with their knowledge and experience, they can pursue lucrative career opportunities in reputed organizations.

The Prolific Automation Course Data Science provides students with the tools necessary to understand the latest trends in machine learning. The course covers topics such as data structures, algorithms, and machine learning. Students will gain a solid foundation in mathematics and computer science. The course is also designed to provide students with valuable professional development skills. Additionally, it covers presentation skills, job searches, and graduate study. There are more than 30 prerequisites for the course.

The course also gives students hands-on experience in applying data science algorithms. They work on small teams to solve internal challenges – many sponsored by local companies. And they represent the university in external challenges, in which they form large teams. They will be expected to participate in both types of challenges, attend meetings, and present short presentations. You must have a bachelor’s degree in any field to enroll in the course. This training will prepare you to apply data science to real-world situations.


Python Basic


1. Introduction Of Python And Comparison With Other Programming Language

2. Installation Of Python And Ide

3. Python Data Types

4. Variable

5. Keywords

6. Operators

Arithmetic operators 

Assignment Operators

Comparison Operators

Logical Operators 

Identity Operators 

Membership Operators

String Objects





String Method

List Object Basics

  1. Definition
  2. Concatenation
  3. Indering,Slicing
  4. Methods

Taking an User input


  1. Definition
  2. Concatenation
  3. Indexing, SLicing
  4. Methods

Shallow Copy

Conditional  Statements

  1. if
  2. else
  3. elif -> else if
  4. nested if else block

Loops in Python

  1. Forloop
  2. While loop
  3. Loop controlling statements
  4. Nested loops

Pattern Printing


  1. Definition
  2. Properties 
  3. Methods
  4. Frozenset


  1. Definition
  2. Forming a dictionary using diffrent technique
  3. Dictionary Methods
  4. Iterating Over A Dictiionary


  1. List
  2. Tuple
  3. 3. Set
  4. Dectionary

Python Functions

  1. Built-in functions
  2. User defwed functions


  1. Why Modules
  1. Importing  Modules
  2. Standard  Modules
  3. Third party Modules


Oops Concepts

  1. Oops concepts
  2. Creating classes
  3. Pillars of oops
  4. lnbe1itance
  5. Polymorphism
  6. Encapsulation
  7. Abstraction
  8.  Decorator
  9. Special(magic/dundermethods
  10. Iteratord Decorators,Generators

Python Advance

Regular Expressions(RegExl)
I. Need of RegEx
2. Re Module
3. Re module functions & Methods

4. Introduction
5. Components of Selenium
6. Application s and Uses
7. Limitations

Memory Management

  1. Multithreading
  1. Multiprocessing

File Handling

  1. Working with files
  2. Reading and writing files
  1. ,Decorators ,Generators

Exception Handling Difference Between Exceptions And Error
1. Exceptions handling with try-except

Gui Framework
1. What is desktop and standalone application
2. Use of desktop app
3. Tkinter



    • Data Definition Language (DDL} Statements.
    • Data Manipulation Language {DML) Statements.
    • Data Control Language(DCL) Statements
    • Transaction Control Statements.

2.Monao dB

Flask/Dj ango
1. Introduction
2. Project

Pandas Basic
I. Python pandas – series
2. Python pandas – data frame
3. Python pandas -panel
4. Python pandas – basic functionality
5. Reading data from different file system
Python Numpy

  1. Numpy – ND array object.
  2.  Numpy – data types.
  3.  Numpy – array attributes.
  4.  Numpy – array creation routines.
  5. Numpy – array from existing.
  6. Data array from numerical ranges.
  7.  Numpy – indexing & slicing
  8. Numpy -advanced indexing.
  9. Numpy – iterating over array.
  10. Numpy – array manipulation
  11. Numpy – binary operators.
  12. Numpy – string functions.
  13. Numpy – mathematical functions.
  14. Numpy – arithmetic operations.
  15. Numpy – statistical functions.

Python Projects


Statistics Basic

1. Introduction to basic statistics terms
2. Types Of Statistics
3. Types of data
4. Levels Of Measurment
5. Measures Of Central Tendency tendency
6. Measures Of dispersion
7. Random variables
8. Set
9. Skewness
IO.Covariance and correlation

Probability Distribution Function
1. Probability density/distribution function
2. Types of the probability distribution
3. Binomial Distribution
4. Poisson Distribution
5. Normal Distribution (Gaussian Distribution)
6. Probability density function and mass function
7. Cumulative density function
8. Examples of normal Distribution
9. Bernoulli Distribution 10.Uniform Distribution
11. Z stats
12. Central limit theorem

Statistics Advance

  1. Hypothesis
  2.  Hypothesis testing’s mechanism
  3.  P-value
  4.  T-stats
  5.  T-stats vs. Z-stats: overview
  6.  When to use a t-tests vs. Z-tests
  7. Type 1 type 2 error
  8.  Bayes statistics (Bayes theorem)
  9. Confidence interval(ci)
  10. Confidence intervals and the margin of error
  11. Interpreting confidence levels and confidence intervals
  12.  Chi-square test
  13.  Chi-square Distribution using python
  14.  Chi-square for goodness of fit test
  15. When to use which statistical Distribution?
  16. Analysis of variance (anova)
  17. Assumptions to use anova
  18. Anova three type
  19. Partitioing of Variance in the anova

Solving Stats Problem with Python Data Analysis & Visualization
1. Matplorlib
2. Data Wrangling
3. Seabom
4. Tableau

Machine Learning
1. Introduction
2. Ai VS ml di VS ds
3. Supervised, unsupervised, semi- supervised, reinforcement tea ming
4. Train, test,validation split
5. Performance
6. Overfitting,under fitting
7. Bias vs variance
8. Feature Enginee1ing
9. Feature Selection IO.Exploratory Data Analysis 11.Regression
I2.Logistic Regression 13.Decision Tree
I4.Support Vector Machines

I5.Nalve Bayes

I6.Ensemble Techniques And Its Types 




20.Dimensionality Reduction 21.Clustering
22.Anomaly Detection
Deep Learning
1. Introduction
2. Neural Network
3. ANN


1. ML Projects
2. Computer Vision Project
3. Mini NLP Project

One Industry level project
1. HLD
2. LLD