Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Data science is a method for transforming business data into assets that help organizations improve revenue, reduce costs, seize business opportunities, improve customer experience, and more.
“The amount of data you can grab, if you want, is immense, but if you’re not doing anything with it, turning it into something interesting, what good is it? Data science is about giving that data a purpose”
By the end of this blog, you will be able to understand what is Data Science and its role in extracting meaningful insights from the complex and large sets of data all around us. You’ll also know why Data Science is so crucial and then the journey you must take to make a career in it.
Data Science Definition
Data science, in its most basic terms, can be defined as obtaining insights and information, really anything of value, out of data.
As per Wikipedia : Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. It is a “concept to unify statistics, data analysis, machine learning, domain knowledge and their related methods” in order to “understand and analyze actual phenomena” with data. It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, domain knowledge and information science.
What is Data Science ?
Commonly referred to as the “oil of the 21st century,” our digital data carries the most importance in the field. It has incalculable benefits in business, research and our everyday lives. Your route to work, your most recent Google search for the nearest coffee shop, your Instagram post about what you ate, and even the health data from your fitness tracker are all important to different data scientists in different ways. Sifting through massive lakes of data, looking for connections and patterns, data science is responsible for bringing us new products, delivering breakthrough insights and making our lives more convenient.
Lets look at this video that explains Data Science in Short :
History of Data Science
In its early days in the 60s, the term data science was often used as an alternative to computer science. It was probably used for the first time by Peter Naur in 1960 and later published by him in 1974 in Concise Survey of Computer Methods. However, it was used for the first time officially at the Kobe Conference in 1996 of the International Federation of Classification Societies, where it was actually used to define the event itself.
Following the newfound popularity of this term, Professor C. F. Jeff Wu used the term data science in the title of his inaugural lecture at the University of Michigan. The title was Statistics = Data Science? Immediately, this title gave impetus to the term. The lecture became popular within the sphere of mathematicians and statisticians, and was further used as part of his program to honor the Indian statistician Prasanta Chandra Mahalanobis, who founded the Indian Statistical Institute.
Since then the term has been used on various prestigious platforms, including the International Council for Science: Committee on Data for Science and Technology in 2002, the magazine The Journal of Data Science founded by the University of Columbia in 2003, the report titled Long-lived Digital Data Collections put out by The National Science Board in 2005 and many others.
Read the below article to know further: https://www.forbes.com/sites/gilpress/2013/05/28/a-very-short-history-of-data-science/#19a4d61755cf
Why is Data Science Important ?
By 2020, there will be around 40 zettabytes of data—that’s 40 trillion gigabytes. The amount of data that exists grows exponentially. This means there is a huge amount of work in data science—much left to uncover. Simple data analysis can interpret data from a single source, or a limited amount of data. However, data science tools are critical to understanding big data and data from multiple sources in a meaningful way.
Data science enables businesses to process huge amounts of structured and unstructured big data to detect patterns. This in turn allows companies to increase efficiencies, manage costs, identify new market opportunities, and boost their market advantage.
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Real Life applications of Data Science
Asking a personal assistant like Alexa or Siri for a recommendation demands data science. So does operating a self-driving car, using a search engine that provides useful results, or talking to a chatbot for customer service. These are all real-life applications for data science.
Terms related to Data Science
Machine learning is the backbone of data science. Data Scientists need to have a solid grasp on ML in addition to basic knowledge of statistics.
Mathematical models enable you to make quick calculations and predictions based on what you already know about the data. Modeling is also a part of ML and involves identifying which algorithm is the most suitable to solve a given problem and how to train these models.
Statistics are at the core of data science. A sturdy handle on statistics can help you extract more intelligence and obtain more meaningful results.
Some level of programming is required to execute a successful data science project. The most common programming languages are Python, and R. Python is especially popular because it’s easy to learn, and it supports multiple libraries for data science and ML.
A capable data scientist, you need to understand how databases work, how to manage them, and how to extract data from them.
Regression is an ML algorithm based on supervised learning techniques. The output of regression is a real or continuous value. For example, predicting the temperature of a room.
Clustering is an ML algorithm based on unsupervised learning techniques. It works on a set on unlabeled data points and groups each data point into a cluster.
A decision tree refers to a supervised learning method used primarily for classification. The algorithm classifies the various inputs according to a specific parameter. The most significant advantage of a decision tree is that it is easy to understand, and it clearly shows the reason for its classification.
Support Vector Machines
Support vector machines (SVMs) is also a supervised learning method used primarily for classification. SVMs can perform both linear and non-linear classifications.
Naive Bayes is a statistical probability-based classification method best used for binary and multi-class classification problems.
What is Data Science used for?
Data science helps us achieve some major goals that either were not possible or required a great deal more time and energy just a few years ago, such as:
- Anomaly detection (fraud, disease, crime, etc.)
- Automation and decision-making (background checks, credit worthiness, etc.)
- Classifications (in an email server, this could mean classifying emails as “important” or “junk”)
- Forecasting (sales, revenue and customer retention)
- Pattern detection (weather patterns, financial market patterns, etc.)
- Recognition (facial, voice, text, etc.)
- Recommendations (based on learned preferences, recommendation engines can refer you to movies, restaurants and books you may like)
- Actionable insights (via dashboards, reports, visualizations)
- Segmentation (e.g., demographic-based marketing)
- Optimization (e.g., risk management)
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Industry wise usage of Data Science
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.
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.
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.
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.
Machine learning and data science have saved the financial industry millions of dollars, and unquantifiable 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.
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.
Data Science used by Big Companies
Google is by far the biggest company that is on a hiring spree for trained Data Scientists. Since Google is mostly driven by Data Science, Artificial Intelligence, and Machine Learning these days, it offers one of the best Data Science opportunities to its employees.
Amazon is a global e-commerce and cloud computing giant that is hiring Data Scientists on a big scale. They need Data Scientists to find out customer mindset and enhance the geographical reach of both e-commerce and cloud domains, among other business-driven goals.
An online financial gateway for most companies, Visa does transactions worth hundreds and millions in a single day. Due to this, the need for Data Scientists is huge at Visa to generate more revenue, check fraudulent transactions, and customize products and services as per customer requirements, etc.
Netflix uses data science in providing suggestions based on your interest or on your previous search.
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Data Science Life Cycle
The image represents the five stages of the data science life cycle:
- Capture, (data acquisition, data entry, signal reception, data extraction)
- Maintain (data warehousing, data cleansing, data staging, data processing, data architecture)
- Process (data mining, clustering/classification, data modeling, data summarization)
- Analyze (exploratory/confirmatory, predictive analysis, regression, text mining, qualitative analysis)
- Communicate (data reporting, data visualization, business intelligence, decision making).
Technologies used in Data Science
- Python is a programming language with simple syntax that is commonly used for data science. There are a number of python libraries that are used in data science including numpy, pandas, and scipy.
- R is a programming language that was designed for statisticians and data mining and is optimized for computation.
- TensorFlow is a framework for creating machine learning models developed by Google.
- Pytorch is another framework for machine learning developed by Facebook.
- Jupyter Notebook is an interactive web interface for Python that allows faster experimentation.
- Tableau makes a variety of software that is used for data visualization.
- Apache Hadoop is a software framework that is used to process data over large distributed systems.
Phases of a Data Science Project
Phase 1 – Discovery
Before you begin the project, it is important to understand the various specifications, requirements, priorities and required budget. You must possess the ability to ask the right questions. Here, you assess if you have the required resources present in terms of people, technology, time and data to support the project. In this phase, you also need to frame the business problem and formulate initial hypotheses (IH) to test.
Phase 2 – Data preparation
In this phase, you require analytical sandbox in which you can perform analytics for the entire duration of the project. You need to explore, preprocess and condition data prior to modeling. Further, you will perform ETLT (extract, transform, load and transform) to get data into the sandbox. You can use R for data cleaning, transformation, and visualization. This will help you to spot the outliers and establish a relationship between the variables. Once you have cleaned and prepared the data, it’s time to do exploratory analytics on it.
Phase 3—Model planning
Here, you will determine the methods and techniques to draw the relationships between variables. These relationships will set the base for the algorithms which you will implement in the next phase. You will apply Exploratory Data Analytics (EDA) using various statistical formulas and visualization tools.
Now that you have got insights into the nature of your data and have decided the algorithms to be used. In the next stage, you will apply the algorithm and build up a model.
Phase 4—Model building
In this phase, you will develop datasets for training and testing purposes. You will consider whether your existing tools will suffice for running the models or it will need a more robust environment (like fast and parallel processing). You will analyze various learning techniques like classification, association and clustering to build the model.
In this phase, you deliver final reports, briefings, code and technical documents. In addition, sometimes a pilot project is also implemented in a real-time production environment. This will provide you a clear picture of the performance and other related constraints on a small scale before full deployment.
Phase 6—Communicate results
Now it is important to evaluate if you have been able to achieve your goal that you had planned in the first phase. So, in the last phase, you identify all the key findings, communicate to the stakeholders and determine if the results of the project are a success or a failure based on the criteria developed in Phase 1.
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Why Learn Data Science?
Data science is a growing field. A career as a data scientist is ranked at the third best job in America for 2020 by Glassdoor, and was ranked the number one best job from 2016-2019. Here are the reasons that will surely convince you to make a career in Data Science-
A fuel of 21st Century
In the last century, oil was considered as the ‘black gold’. But, with the industrial revolution and the emergence of the automotive industry, oil became the main driving source of human civilization. However, with time, its value dwindled due to the gradual exhaustion and resorting to alternative renewable sources of energy.
In the 21st century, the new driving force behind industries is Data. Industries are using data to impart autonomy and improve the safety of their vehicles. The idea is to create powerful machines that think in the form of data.
Problem of Demand & Supply
There is a huge abundance of data. However, there aren’t enough resources to convert this data into useful products. That is, there aren’t enough people who possess the required skills to help companies utilize the potential that data holds. Due to this reason, there is a dearth in the supply of Data Scientists.
Much of this is contributed by the infancy of Data Science as a field. There is a lack of ‘data-literacy’ in the market. In order to fill this vacuum in supply, you need to learn Data Science and its underlying fields.
A Lucrative Career
The average salary for a Data Scientist is $117,345/yr. This is above the national average of $44,564. Therefore, a Data Scientist makes 163% more than the national average salary. This makes Data Science a highly lucrative career choice. It is mainly due to the dearth in Data Scientists resulting in a huge income bubble.
Since Data Science requires a person to be proficient and knowledgeable in several fields like Statistics, Mathematics and Computer Science, the learning curve is quite steep. Therefore, the value of a Data Scientist is very high in the market.
Data Science can make the World a Better Place
Big Data & Data Science is beyond being a tool of Business Intelligence. Various philanthropic and social organizations are using data to create products for social good. Also, various health-care organizations are using data for helping doctors to have better insights about their patient’s health.
Data Science is the Career of Tomorrow
Data Science is the career of the future. Industries are becoming data-driven and new innovations are being made every day. The field of technology has become dynamic and with more and more people interacting with the internet, more data is being generated. Industries require data-scientists to assist them in making smarter decisions and creating better products. Data perceives as the electricity of modern gadgets and applications. It makes products smart and empowers them with autonomy.
Even if you’re not interested in becoming a data scientist, learning data skills and improving your data literacy can pay big dividends in your current career. Employees who have data skills and can help their companies become more data driven are in demand across almost any industry.
Future of Data Science
As the field evolves, we can expect to see several trends shaping the future of data science. First, more data science tasks in the life-cycle will likely become automated. This change will be driven by pressure to increase ROI as more businesses invest in machine learning and AI. With more data science processes automated, more data will be usable to more people in more verticals—and AI and machine learning should progress more quickly, too.
An Interesting trend which will likely shape the future of data science is tension between the right to privacy, the need to regulate, and the demand for transparency. Data science has the power to make machine learning algorithms and the process through which we train AIs far more transparent, which can in turn make regulatory oversight possible.
It will be interesting to see how many more verticals where data science is used will open up as automation paves the way.
Limitations and Criticism of Data Science
There has been some criticism of the concept of data science as well. This has more to do with the methods that are employed in the collection of data rather than the definition of the concept itself. According to some experts, the methods that are employed to obtain the data that is recorded cannot be relied upon. According to them, they might not be trustworthy, and moreover, the methods used for assimilating data are dubious, since they might be heavily influenced by geography, time, and other related factors.
The term itself has met with a lot of criticism. A few experts maintain that data science has always existed since the development of the computer in the 60s; however, now it has become kind of a catchphrase for people to describe their job profiles, and perhaps even to make it feel more enhanced. These critics maintain that data science in reality does not fit into any clear definition and as such the very claim that it is science is under a cloud of aspersion.
Some people have even maintained that the method is non-statistical and as such it does not give a clear picture of the information that these serious spheres of activity must look for. An interpretation of this statement is that data science is actually unscientific and thus it can do more hurt than harm.
However, at the same time, it must be remembered that data science is still a growing industry. As more and more methods are being invented, the definition of this concept is becoming clearer, and a greater degree of accuracy is coming into play than it existed before. In this age of technology, data science is something that has become the need of the hour, and it is only a matter of time before it becomes a part and parcel of daily human lives.
Who is a Data Scientist ?
What profession did Harvard call the Sexiest Job of the 21st Century? That’s right… the data scientist. The term “data scientist” was coined as recently as 2008 when companies realized the need for data professionals who are skilled in organizing and analyzing massive amounts of data.
What does Data Scientist Do ?
Effective data scientists are able to identify relevant questions, collect data from a multitude of different data sources, organize the information, translate results into solutions, and communicate their findings in a way that positively affects business decisions. These skills are required in almost all industries, causing skilled data scientists to be increasingly valuable to companies.
Specific tasks include:
- Identifying the data-analytics problems that offer the greatest opportunities to the organization
- Determining the correct data sets and variables
- Collecting large sets of structured and unstructured data from disparate sources
- Cleaning and validating the data to ensure accuracy, completeness, and uniformity
- Devising and applying models and algorithms to mine the stores of big data
- Analyzing the data to identify patterns and trends
- Interpreting the data to discover solutions and opportunities
- Communicating findings to stakeholders using visualization and other means
Roles and Responsibilities of a Data Scientist
Data scientists work closely with business stakeholders to understand their goals and determine how data can be used to achieve those goals. They design data modeling processes, create algorithms and predictive models to extract the data the business needs, then help analyze the data and share insights with peers.
- Ask the right questions to begin the discovery process.
- Acquire data.
- Process and clean the data.
- Integrate and store data.
- Initial data investigation and exploratory data analysis.
- Choose one or more potential models and algorithms
- Apply data science methods and techniques, such as machine learning, statistical modeling, and artificial intelligence.
- Measure and improve results.
- Present final results to stakeholders.
- Make adjustments based on feedback.
- Repeat the process to solve a new problem.
How to become a Data Scientist ?
If you feel excited about data science, now is a perfect time to start exploring. Stats suggest that data science skills are in high demand and making the career transition can happen in as little as 6 months. Granted those will probably be the 6 toughest months of your life, but in the end, and trust us on this one, it will be worth it. Data scientists are some of World’s best paid and happiest workers.
Education-wise, there is no single path to becoming a data scientist. Many universities have created data science and analytics-specific programs, mostly at the master’s degree level. Some universities and other organizations also offer certification programs as well.
In addition to traditional degree and certification programs, there are bootcamps being offered that range from a few days or months to complete, online self-guided learning and MOOC courses focused on data science and related fields, and self-driven hands-on learning.
In order to become a data scientist, there is a significant amount of education and experience required. The first step in becoming a data scientist is to earn a bachelor’s degree, typically in a field related to computing or mathematics. Coding bootcamps are also available and can be used as an alternate pre-qualification to supplement a bachelor’s degree in another field. Most data scientists also complete a master’s degree or a PhD in data science. Once these qualifications are met, the next step to becoming a data scientist is to apply for an entry-level job in the field. Some data scientists may later choose to specialize in a sub-field of data science.
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Skills needed to be a Data Scientist
Data scientists need to be curious and result-oriented, with exceptional industry-specific knowledge and communication skills that allow them to explain highly technical results to their non-technical counterparts. They possess a strong quantitative background in statistics and linear algebra as well as programming knowledge with focuses in data warehousing, mining, and modeling to build and analyze algorithms.
Since computer programming is a large component, data scientists must be proficient with programming languages such as R, Python, SQL, Scala, Julia, Java, and so on. Usually it’s not necessary to be an expert programmer in all of these, but R, Python, and SQL are definitely key, and others like Scala for big data are becoming more prominent as well.
For statistics, mathematics, algorithms, modeling, and data visualization, data scientists usually use pre-existing packages and libraries where possible. Some of the more popular ones include Scikit-learn, e1071, Pandas, Numpy, TensorFlow, Matplotlib, D3, Shiny, and ggplot2.
For reproducible research and reporting, data scientists commonly use notebooks and frameworks such as Jupyter, iPython, Knitr, and R markdown. These are very powerful in that the code and data can be delivered along with key results so that anyone can perform the same analysis, and build on it if desired.
More and more these days, data scientists should be able to utilize tools and technologies associated with big data as well. The most popular examples include Hadoop, Spark, Hive, Pig, Drill, Presto, Mahout, and so on.
Finally, data scientists should know how to access and query many of the top RDBMS, NoSQL, and NewSQL database management systems. Some of the most common are MySQL, PostgreSQL, Redshift, MongoDB, Redis, Hadoop, and HBase.
It’s a job that not only requires technical skills, but also “soft skills” that allow successful interactions with people from multiple departments, including business development, sales, product management, project management, UX/UI designs, and software engineering teams.
Summarising, the Skills needed to be a Data Scientist are :
- Statistical analysis
- Machine learning
- Computer science
- Data storytelling
- Business intuition
- Analytical thinking
- Critical thinking
- Interpersonal skills
Why Data Science is a good career option ?
Glassdoor ranked data scientist as the #1 Best Job in America in 2018 for the third year in a row. As increasing amounts of data become more accessible, large tech companies are no longer the only ones in need of data scientists. The growing demand for data science professionals across industries, big and small, is being challenged by a shortage of qualified candidates available to fill the open positions.
The need for data scientists shows no sign of slowing down in the coming years. LinkedIn listed data scientist as one of the most promising jobs in 2017 and 2018, along with multiple data-science-related skills as the most in-demand by companies.
Looking at the Figures in 2020 :
28% : Demand Increase by 2020
4,524 : Number of Job Openings
$120,931 : Average Base Salary
#1 : Best Job in America
Data Scientist Average Base Salary
Data analyst: $65,47
Data scientist: $120,931
Senior data scientist: $141,257
Data engineer: $137,776
Titles similar to Data Scientist
- Machine Learning Scientist: Machine learning scientists research new methods of data analysis and create algorithms.
- Data Analyst: Data analysts utilize large data sets to gather information that meets their company’s needs.
- Data Consultant: Data consultants work with businesses to determine the best usage of the information yielded from data analysis.
- Data Architect: Data architects build data solutions that are optimized for performance and design applications.
- Applications Architect: Applications architects track how applications are used throughout a business and how they interact with users and other applications.
- Data engineers: Clean, aggregate, and organize data from disparate sources and transfer it to data warehouses.
- Business intelligence specialists: Identify trends in data sets.
Best Data Science Blogs
Best Data Science Books
A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges.
Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You’ll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples—ready for immediate application to your next graph or presentation.
To really learn data science, you should not only master the tools—data science libraries, frameworks, modules, and toolkits—but also understand the ideas and principles underlying them. Updated for Python 3.6, this second edition of Data Science from Scratch shows you how these tools and algorithms work by implementing them from scratch.
Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the “data-analytic thinking” necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today.
Created with the beginner in mind, this powerful bundle delves into the fundamentals behind Python and data science, from basic code and concepts to complex neural networks and data manipulation. Inside, you’ll discover everything you need to know to get started with Python and data science, and begin your journey to success!
Best Data Science Courses and Certification
Massive Open Online Courses (MOOCs) are a useful way for people to gain necessary educational training and experience, especially with everyone locked down due to COVID-19. Bootcamps can also prove useful, if you’re willing to make the time and money commitment.
Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included. This course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models. The course will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.
Learn how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn , Machine Learning, Tensorflow , and more! Learn how to program with Python, how to create amazing data visualizations, and how to use Machine Learning with Python!
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning. It is the most effective, time-efficient, and structured data science training available online. I solves the biggest challenge to entering the data science field – having all the necessary resources in one place.
Learn Programming In R And R Studio. Data Analytics, Data Science, Statistical Analysis, Packages, Functions, GGPlot2. This course has been designed for all skill levels and even if you have no programming or statistical background you will be successful in this Data Science course!
Learn Data Science step by step through real Analytics examples. Data Mining, Modeling, Tableau Visualization and more! In this course you WILL experience firsthand all of the PAIN a Data Scientist goes through on a daily basis. Corrupt data, anomalies, irregularities – you name it! This course will give you so much practical exercises that real world will seem like a piece of cake when you graduate this class.
Statistics you need in the office: Descriptive & Inferential statistics, Hypothesis testing, Regression analysis. You will acquire the fundamental skills that will enable you to understand complicated statistical analysis directly applicable to real-life situations.
Learn how to use the R programming language for data science and machine learning and data visualization! You will learn how to program with R, how to create amazing data visualizations, and how to use Machine Learning with R. This course is designed for both complete beginners with no programming experience or experienced developers looking to make the jump to Data Science!
Learn The Core Stats For A Data Science Career. Master Statistical Significance, Confidence Intervals And Much More! Master topics such as distributions, the z-test, the Central Limit Theorem, hypothesis testing, confidence intervals, statistical significance and many more!
A primer on Machine Learning for Data Science. Revealed for everyday people, by the Backyard Data Scientist. In this introductory course, the “Backyard Data Scientist” will guide you through wilderness of Machine Learning for Data Science. Accessible to everyone, this introductory course not only explains Machine Learning, but where it fits in the “techno sphere around us”, why it’s important now, and how it will dramatically change our world today and for days to come.
Learn Data Science, Data Analysis, Machine Learning (Artificial Intelligence) and Python with Tensorflow, Pandas & more! This comprehensive and project based course will introduce you to all of the modern skills of a Data Scientist and along the way, we will build many real world projects to add to your portfolio. You will get access to all the code, workbooks and templates (Jupyter Notebooks) on Github, so that you can put them on your portfolio right away!
Best Data Science Programs
What you will learn
- Fundamental R programming skills
- Statistical concepts such as probability, inference, and modeling and how to apply them in practice
- Gain experience with the tidyverse, including data visualization with ggplot2 and data wrangling with dplyr
- Become familiar with essential tools for practicing data scientists such as Unix/Linux, git and GitHub, and RStudio
- Implement machine learning algorithms
- In-depth knowledge of fundamental data science concepts through motivating real-world case studies
What you will learn
- Apply various Data Science and Machine Learning skills, techniques, and tools to complete a project and publish a report.
- Practice with various tools used by Data Scientists and become experienced in using some of them like Jupyter notebooks.
- Master the key steps involved in tackling a data science problem and learn to follow a methodology to think and work like a Data Scientist.
- Write SQL to query databases and explore relational database concepts.
- Understand Python and practice Python programming using Jupyter.
- Import and clean data sets, analyze data, build and evaluate data models and pipelines using Python.
- Utilize several data visualization tools, techniques and libraries in Python to present data visually.
- Understand and apply various supervised and unsupervised Machine Learning models and algorithms to address real world challenges using Python.
What you will learn
- Master the foundations of data science, statistics, and machine learning
- Analyze big data and make data-driven predictions through probabilistic modeling and statistical inference; identify and deploy appropriate modeling and methodologies in order to extract meaningful information for decision making
- Develop and build machine learning algorithms to extract meaningful information from seemingly unstructured data; learn popular unsupervised learning methods, including clustering methodologies and supervised methods such as deep neural networks
- Finishing this MicroMasters program will prepare you for job titles such as: Data Scientist, Data Analyst, Business Intelligence Analyst, Systems Analyst, Data Engineer
What you will learn
- How to interpret and communicate data and results using a vast array of real-world examples from different domains
- How to make predictions using machine learning and statistical methods
- Computational thinking and skills, including the Python programming language for analyzing and visualizing data
- How to think critically about data and draw robust conclusions based on incomplete information
What you will learn
- The history of data science, tangible illustrations of how data science and analytics are used in decision making across multiple sectors today, and expert opinion on what the future might hold
- A practical understanding of the fundamental methods used by data scientists including; statistical thinking and conditional probability, machine learning and algorithms, and effective approaches for data visualization
- The major components of the Internet of Things (IoT) and the potential of IoT to totally transform the way in which we live and work in the not-to-distant future
- How data scientists are using natural language processing (NLP), audio and video processing to extract useful information from books, scientific articles, twitter feeds, voice recordings, YouTube videos and much more
Best Degree Courses in Data Science
Most universities offering a Degree in Data Science prepares students by applying the principles, tools, and methods of Data Science to a project within a sponsoring organization. Graduates complete the program with a core analytical skill set upon which to layer more specialized technical or industry-specific applications. Experiential learning is a key component of the program. Students learn by building portfolios of real-world projects, demonstrating competency with key technologies, visualization, and communication techniques, and the ability to translate information into recommended actions.
The Department of Statistics and Data Sciences at The University of Texas at Austin has partnered with the Department of Computer Science to offer a Master of Science in Data Science. This new online master’s program embodies the defining principles of Data Science.
Course curriculum incorporates ideas and methods such as simulation, data visualization, data mining, data analysis, large scale data-based inquiry for big data, and non-standard design methodologies, along with topics of machine learning, algorithmic techniques, and optimization, to tackle issues that come up with large-scale data such as memory and computational speed.
The PG diploma is an engaging yet rigorous 12-month online program designed specifically for working professionals to develop practical knowledge and skills, establish a professional network, and accelerate entry into data science careers. The certification is awarded by IIIT Bangalore. Expect to carry out several industry-relevant projects simulated as per the actual workplace, making you a skilled data science professional at par with leading industry standards. Specializations:
- Deep Learning: Advanced Machine Learning, Neural Networks
- Natural Language Processing: Advanced Machine Learning, Natural Language Processing
- Business Intelligence: Advanced SQL and NoSQL Databases, Storytelling with Advanced Visualization
- Business Analytics: Advanced Machine Learning, Storytelling and Advanced Business Problem Solving
- Data Engineering: Data Modelling and Data Warehousing, Building Data Pipelines, Data Streaming and Processing
In this MSDS program, students gain critical skills for succeeding in today’s data-intensive world. They learn how to utilize relational and document database systems and analytics software built upon open-source systems such as R, Python, and TensorFlow. They learn how to make trustworthy predictions using traditional statistics and machine learning methods.
Question to ask before enrolling in any Data Science Program
- Is the curriculum self-paced – or is there a schedule?
- Will I get a certificate of completion?
- Is there a refund policy?
- Are there any prerequisites for taking the courses? Do I need prior experience or an academic degree?
- What is the time frame required to complete the whole program?
- Can I access the program via mobile?
- How can I track my overall progress in the program?
- Is the certificate Verifiable ?
- How is the Help and Support model ?
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Data Science vs Other Domains
Data Scientist vs Data Analyst
A Data Analyst usually explains what is going on by processing history of the data. On the other hand, Data Scientist not only does the exploratory analysis to discover insights from it, but also uses various advanced machine learning algorithms to identify the occurrence of a particular event in the future. A Data Scientist will look at the data from many angles, sometimes angles not known earlier.
Data Science vs Business Intelligence
BI basically analyzes the previous data to find hindsight and insight to describe the business trends. BI enables you to take data from external and internal sources, prepare it, run queries on it and create dashboards to answer the questions like quarterly revenue analysis or business problems. BI can evaluate the impact of certain events in the near future.
Data Science is a more forward-looking approach, an exploratory way with the focus on analyzing the past or current data and predicting the future outcomes with the aim of making informed decisions. It answers the open-ended questions as to “what” and “how” events occur.
Data Science vs Statistics
Many statisticians, have argued that data science is not a new field, but rather another name for statistics. Others argue that data science is distinct from statistics because it focuses on problems and techniques unique to digital data.
Andrew Gelman of Columbia University and data scientist Vincent Granville have described statistics as a nonessential part of data science.
Stanford professor David Donoho writes that data science is not distinguished from statistics by the size of datasets or use of computing, and that many graduate programs misleadingly advertise their analytics and statistics training as the essence of a data science program. He describes data science as an applied field growing out of traditional statistics.
In summary, data science can be therefore described as an applied branch of statistics.
Data Science vs Artificial Intelligence
Artificial intelligence powers various real-world applications by enabling faster and more error-proof outcomes across different fields. A subset of AI, machine learning helps make these applications more accurate with the help of data. Data Science, on the other hand, makes use of ML – and other technologies like cloud computing, big data analytics, etc – to analyse massive datasets to extract insights and make future predictions. In other words, data science uses AI as a tool to solve problems for organisations.
Data Scientist vs Data Engineer
The data engineer prepares data sets for the data scientist to work with and draw insights from, but the intelligent analysis work falls to data scientists, not “data science engineers.”
Data Science vs Data Mining
Data mining is a technique used in business and data science both, while data science is an actual field of scientific study or discipline. Data mining’s goal is to render data more usable for a specific business purpose. Data science, in contrast, aims to create data-driven products and outcomes—usually in a business context.
Data Science vs Machine Learning
Data science is a natural extension of statistics. It evolved alongside computer science to handle massive amounts of data with the help of new technologies.
In contrast, machine learning is part of data science, but it is more of a process. Machine learning allows computers to learn—and do so more effectively over time—without explicit programs for every bit of information. Machine learning is the field of data science that feeds computers huge amounts of data so they can learn to make insightful decisions similar to the way that humans do.
Data Science vs Deep Learning
Deep learning is a function of AI that mimics how the human brain works as it processes data and generates patterns to use as it makes decisions. Deep learning is an important subset of data science research.
Data Science vs Business Analytics
Both data science and business analytics focus on solving business problems, and both involve collecting data, modeling it, and then gleaning insights from the data. The main difference is that business analytics is specific to business-related problems such as profit and costs. In contrast, data science methods explore how a wide range of factors—anything from customer preferences to the weather—might affect a business.
R vs Python for Data Science
Data scientists need tools for data transformation, data cleaning, and data visualization. There is also a need to detect outliers, identify relationships between variables, and construct complete interpretive models inside a suitable environment. This is where data preparation and statistical analysis tools like R and Python come in.
R may be too heavy and slow for your system. It also has difficult syntax, and comes with a learning curve that can be steep. Python was developed as a more readable language for general uses, and it is simpler and more flexible to learn. Another key difference is that R exists mostly within the data science ecosystem, while Python is used in various verticals. The down side to Python for data science is less data visualization power.
Decide based on the data problems you will solve, your ability to learn and master the tool, how much data visualization you expect to do, and the current standards in your specific vertical.
Common Questions around Data Science
What is data science in simple words?
Data science is simply the study of data. It involves developing methods of recording, storing, and analyzing data to effectively extract useful information. The goal of data science is to gain insights and knowledge from any type of data – both structured and unstructured.
What does a data scientist do?
Data scientists work closely with business stakeholders to understand their goals and determine how data can be used to achieve those goals.
What is the salary of data scientist?
Average Annual salary of a Data Scientist in USA is close to 100K USD.
Who is eligible for data science?
The basic requirements for being considered eligible to pursue Data Science include a bachelor’s degree in science, business administration, engineering, computer applications, or mathematics OR a master’s degree in statistics, commerce, or mathematics.
Is Data Scientist an IT job?
Yes, Data Scientist is an IT Job. Data Scientists are responsible for business analytics, they are also involved in building data products and software platforms, along with developing visualizations and machine learning algorithms.
Is Data Science hard?
Learning data science is not hard, but it takes time and patience. It’s a combination of hard skills (like learning Python and SQL) and soft skills (like business skills or communication skills) and more.
Can I learn Data Science on my own?
Yes, you can learn Data Science at your own and become a self taught data scientist. It is harder than a formal education, but with proper approach it is certainly possible.
Do you need math to be a data scientist?
No, You don’t need advanced math to get started with data science. You don’t need calculus or linear algebra. You can learn the essentials of machine learning with rather limited maths background.
Why do data scientists make so much money?
Data Science is a niche today. Companies today are in search of qualified candidates who can help them better understand big data, but these qualified candidates are scarce and hence are paid well.
Is data science good for freshers?
Yes, companies do hire freshers for data analyst and data scientist positions. In fact, most of the entry level analytics jobs don’t need any specialization or post graduation.
Do data scientists code?
Yes. Data scientists, for the most part of their Job Role, involves coding.
Is data science a stressful job?
No Definitely not. Stressful situations are only when you have deliverables that you need to meet. It’s there everywhere. But in terms of day to day job, Data Science jobs are as exciting as any other if it is your passion.
Is it too late to learn data science?
No, It is never too late to Learn data science. There will be a severe shortage of Data Scientists and other Data Science based jobs throughout the world in the coming days. Technology is evolving and changing really fast.
Is it worth getting a data science certification?
The simple answer is ‘Yes, Data Science certificate is worth the time and money’ but to expand on that, it depends on what your level of expertise is.
In this article, we’ve revealed the value of a data scientists, going beyond the benefits of data science to show you exactly why you need a data scientist to tap into the real potential of this enigmatic aspect of modern business and marketing.
Data science can add value to any business who can use their data well. From statistics and insights across workflows and hiring new candidates, to helping senior staff make better-informed decisions, data science is valuable to any company in any industry.
As the importance of data science is increasing day by day, the need for a data scientist is also growing. Data scientist are the future of the world. Thus, a data scientist must be capable of providing great solutions which meets the challenges of all the fields.
On the surface, the statistics and analytical tasks in data science may not seem as exciting as other tech careers, but it is the indispensable foundation on which revolutionary AI, machine learning, and blockchain ventures are built.
Without data, our world wouldn’t have a digital age.
And without data scientists, companies can’t hope to survive digital transformation.