Project based on Data Science:-

Uber Data Analysis Project

Data is the oil for uber. With data analysis tools and great insights, Uber improve its decisions, marketing strategy, promotional offers and predictive analytics.

With more than 15 million rides per day across 600 cities in 65 countries, Uber is growing rapidly with Data Science starting from data visualization and gaining insights that help them to craft better decisions. Data Science tools play a key role in every operation of Uber.

Credit Card Fraud Detection Project 

The credit card fraud detection project uses machine learning and R programming concepts.The aim of this project is to build a classifier that can detect credit card fraudulent transactions using a variety of machine learning algorithms that will be able to discern fraudulent from non-fraudulent ones. 

Movie Recommendation System Project

The aim of this interesting Data Science project including code is to build a recommendation system that recommends movies to the users.

Let’s understand this with an example. Have you ever been on an online streaming platform like Netflix or Amazon Prime? If yes, then you must have noticed that after some time these platform starts recommending you different movies and TV shows according to your genre preference. This project in R programming is designed to help you understand the functioning of how a recommendation system works.

Data Science Applications

Healthcare:-

The healthcare sector, especially, receives great benefits from data science applications.

1. Medical Image Analysis

Procedures such as detecting tumors, artery stenosis, organ delineation employ various different methods and frameworks like MapReduce to find optimal parameters for tasks like lung texture classification.

2. Drug Development

The drug discovery process is highly complicated and involves many disciplines. The greatest ideas are often bounded by billions of testing, huge financial and time expenditure. On average, it takes twelve years to make an official submission.

Advanced Image Recognition:-

You upload your image with friends on Facebook and you start getting suggestions to tag your friends. This automatic tag suggestion feature uses face recognition algorithm. 

In their latest update, Facebook has outlined the additional progress they’ve made in this area, making specific note of their advances in image recognition accuracy and capacity.

Speech Recognition:-

Some of the best examples of speech recognition products are Google Voice, Siri, Cortana etc.

Recommendation Systems:

To create a recommendation system. Example, "suggested friends" on Facebook or suggested videos" on YouTube, everything is done with the help of Data Science.

E-Commerce:-

E-commerce and retail industries have been hugely benefitted by data science. Some of the ways in which data science has transformed the e-commerce industries are-

• For identifying a potential customer base, data science is being heavily utilized.
• Usage of predictive analytics for forecasting the goods and services.
• Data Science is also used for identifying styles of popular products and predicting their trends.
• With data science, companies are optimizing their pricing structures for their consumers.

Disadvantages :-

1. Mastering Data Science is near to impossible

Being a mixture of many fields, Data Science stems from Statistics, Computer Science and Mathematics. 

2. Large Amount of Domain Knowledge Required

Data Science is its dependency on Domain Knowledge. A person with a  considerable background in Statistics and Computer Science will find it difficult to solve Data Science problem without its background knowledge. 

3.challenging problems

Always,needs to face difreent problems related to mathematics,code implementation,managment or analysis.

4.Upfront time & cost

   Required more effort time as well as cost to become experienced  Data scientist.

Adv vs Disadv of Data Science:

Advantage:-

1. It’s in Demand

Data Science is greatly in demand.It is the fastest growing job on Linkedin and is predicted to create 11.5 million jobs by 2026.

2. A Highly Paid Career

Data Science is one of the most highly paid jobs. According to Glassdoor, Data Scientists make an average of $116,100 per year.

3. Data Science is Versatile

There are numerous applications of Data Science. It is widely used in health-care, banking, consultancy services, e-commerce etc.

4. Data Science Makes Data Better

Companies require skilled Data Scientists to process and analyze their data. They not only analyze the data but also improve its quality. 

5. No More Boring Tasks

Data Science has helped various industries to automate redundant tasks.

6. Data Science Makes Products Smarter

Data Science involves the usage of Machine Learning which has enabled industries to create better products tailored specifically for customer experiences.

7. Data Science can Save Lives

Healthcare sector has been greatly improved because of Data Science. 

8. Data Science Can Make You A Better Person

Data Science will not only give you a great career but will also help you in personal growth.

Data science tools

1. SAS

It is one of those data science tools which are specifically designed for statistical operations. 

2. Apache Spark

Apache Spark or simply Spark is an all-powerful analytics engine and it is the most used Data Science tool. 

3. BigML

BigML, it is another widely used Data Science Tool. It provides a fully interactable, cloud-based GUI environment that you can use for processing Machine Learning Algorithms. 

4. D3.js

Javascript is mainly used as a client-side scripting language. D3.js, a Javascript library allows you to make interactive visualizations on your web-browser. 

5. MATLAB

MATLAB is a multi-paradigm numerical computing environment for processing mathematical information.

6. Excel

Probably the most widely used Data Analysis tool. Microsoft developed Excel mostly for spreadsheet calculations and today, it is widely used for data processing, visualization, and complex calculations.

7. ggplot2

ggplot2 is an advanced data visualization package for the R programming language. 

8. Tableau

Tableau is a Data Visualization software that is packed with powerful graphics to make interactive visualizations.

 It is focused on industries working in the field of business intelligence.

9. Jupyter

Project Jupyter is an open-source tool based on IPython for helping developers in making open-source software and experiences interactive computing. 

10. Matplotlib

Matplotlib is a plotting and visualization library developed for Python. 

It is the most popular tool for generating graphs with the analyzed data.

11. NLTK

Natural Language Processing has emerged as the most popular field in Data Science. 

It deals with the development of statistical models that help computers understand human language. 

12. Scikit-learn

Scikit-learn is a library based in Python that is used for implementing Machine Learning Algorithms.

13. TensorFlow

TensorFlow has become a standard tool for Machine Learning. It is widely used for advanced machine learning algorithms like Deep Learning.

14. Weka

Weka or Waikato Environment for Knowledge Analysis is a machine learning software written in Java. 

It is a collection of various Machine Learning algorithms for data mining.

Here,the data science tools. 

Data Science Jobs Roles

A Data Scientist is somebody who is inquisitive, who can stare at data and spot trends. It’s almost like a Renaissance individual who really wants to learn and bring change to an organization.”

https://youtu.be/00xLZuxTzQc

Here list of diffrent data science jobs:-

• Data Scientist

One of the hottest job titles that you can proudly flaunt on your business card, is that of a data scientist. Data scientists are as rare as unicorns and get to work every day with the mindset of a curious data wizard. They master a whole range of skills and talents.

• Advanced analytics professional

An advanced analytics professional would typically perform  simulations, predictive analytics, prescriptive analytics, and other forms of advanced analysis.

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  Data Analyst

Data analyst job listings encompasses a large spectrum of responsibilities, right from  creating systems that enable business users to gain insights, to ensuring data quality and governance, and to the performance of actual data analysis.

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  Data Engineer

Making the jobs of data scientists and data analysts easier, is what the data engineers do.

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  Business analyst

Tasks that are very similar to those performed by data analysts can be performed by business analysts. 

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  Database Administrator

A database administrator is responsible for all things related to the  monitoring, operation and maintenance of databases.

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  Business Intelligence Professional

OLAP tools, reports, and dashboards to look at historical trends in data sets are business intelligence professionals.

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  The statistician

The historical leader of data and its insights – the statistician! Replaced by more exotic sounding job titles, and though often forgotten; the statistician represents what the data science field stands for – gaining useful insights from data. 

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  Data Architect

The importance of a data architect’s job is quickly increasing, with the rise of big data. The data architect creates the blueprints for data management systems to integrate, centralize, protect and maintain the data sources. The data architect masters technologies like  Pig, Spark and Hive etc.

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  The Machine Learning Engineer

There exist a number of companies for whom their data or their data analysis platform is their product. If so, the data analysis or machine learning going on can be quite intense.

Data Science Components

Data science consists of three components, that is, organizing, packaging and delivering data.

1. Organizing the data:Organizing is where the planning and execution of the physical storage and structure of the data takes place after applying the best practices in data handling.

2. Packaging the data:Packaging is where the prototypes are created, the statistics is applied and the visualisation is developed. It involves logically as well as aesthetically modifying and combining the data in a presentable form.

3.  Delivering the data:Delivering is where the story is narrated and the value is received. It makes sure that the final outcome has been delivered to the concerned people.