Journey to Data-Science
####Data science can be defined as a blend of mathematics, business acumen, tools, algorithms and machine learning techniques, all of which help us in finding out the hidden insights or patterns from raw data which can be of major use in the formation of big business decisions.
##Who is a Data Scientist ?
Data scientists examine which questions need answering and where to find the related data. They have business acumen and analytical skills as well as the ability to mine, clean, and present data. Businesses use data scientists to source, manage, and analyze large amounts of unstructured data.
##Different Roles in Data Science ?
##Data Scientist
Data scientists examine which questions need answering and where to find the related data. They have business acumen and analytical skills as well as the ability to mine, clean, and present data. Businesses use data scientists to source, manage, and analyze large amounts of unstructured data. Results are then synthesized and communicated to key stakeholders to drive strategic decision-making in the organization.
Skills needed: Programming skills (SAS, R, Python), statistical and mathematical skills, storytelling and data visualization, Hadoop, SQL, machine learning
##Data Analyst
Data analysts bridge the gap between data scientists and business analysts. They are provided with the questions that need answering from an organization and then organize and analyze data to find results that align with high-level business strategy. Data analysts are responsible for translating technical analysis to qualitative action items and effectively communicating their findings to diverse stakeholders.
Skills needed: Programming skills (SAS, R, Python), statistical and mathematical skills, data wrangling, data visualization
##Data Engineer
Data engineers manage exponential amounts of rapidly changing data. They focus on the development, deployment, management, and optimization of data pipelines and infrastructure to transform and transfer data to data scientists for querying.
Skills needed: Programming languages (Java, Scala), NoSQL databases (MongoDB, Cassandra DB), frameworks (Apache Hadoop)
#Acquired Skills for a Data-Scientist
- Research Design
- Data Engineering
- Data Visualization
- Ethics and Privacy
- Machine Learning
- Statistical Analysis
- Mining and Exploring
- Communicating Results
#Applications of Data-Science
##Health Care
Data science has led to a number of breakthroughs in the healthcare industry. With a vast network of data now available via everything from EMRs to clinical databases to personal fitness trackers, medical professionals are finding new ways to understand disease, practice preventive medicine, diagnose diseases faster and explore new treatment options.
##Self-Driving Cars
Tesla, Ford and Volkswagen are all implementing predictive analytics in their new wave of autonomous vehicles. These cars use thousands of tiny cameras and sensors to relay information in real-time. Using machine learning, predictive analytics and data science, self-driving cars can adjust to speed limits, avoid dangerous lane changes and even take passengers on the quickest route.
##Logistics
UPS turns to data science to maximize efficiency, both internally and along its delivery routes. The company’s On-road Integrated Optimization and Navigation (ORION) tool uses data science-backed statistical modeling and algorithms that create optimal routes for delivery drivers based on weather, traffic, construction, etc. It’s estimated that data science is saving the logistics company up to 39 million gallons of fuel and more than 100 million delivery miles each year.
##Entertainment
Do you ever wonder how Spotify just seems to recommend that perfect song you’re in the mood for? Or how Netflix knows just what shows you’ll love to binge? Using data science, the music streaming giant can carefully curate lists of songs based off the music genre or band you’re currently into. Really into cooking lately? Netflix’s data aggregator will recognize your need for culinary inspiration and recommend pertinent shows from its vast collection.
##Finance
Machine learning and data science have saved the financial industry millions of dollars, and quantifiable amounts of time. For example, JP Morgan’s Contract Intelligence (COiN) platform uses Natural Language Processing (NLP) to process and extract vital data from about 12,000 commercial credit agreements a year. Thanks to data science, what would take around 360,000 manual labor hours to complete is now finished in a few hours. Additionally, fintech companies like Stripe and Paypal are investing heavily in data science to create machine learning tools that quickly detect and prevent fraudulent activities.
##Cybersecurity
Data science is useful in every industry, but it may be the most important in cybersecurity. International cybersecurity firm Kaspersky is using data science and machine learning to detect over 360,000 new samples of malware on a daily basis. Being able to instantaneously detect and learn new methods of cybercrime, through data science, is essential to our safety and security in the future.
> # Key Skills of a Data-Scientist
>
> - Statistics.
> - At least one programming language – R/ Python.
> - Data Extraction, Transformation, and Loading.
> - Data Wrangling and Data Exploration.
> - Machine Learning Algorithms.
> - Advanced Machine Learning (Deep Learning)
> - Big Data Processing Frameworks.
> - Data Visualization.
>
> *Everything* is going according to **plan**.
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