Data Science online certification courses [Free & Paid]
What is Data Science:
Data science is the study of data to extract meaningful insights for business. It is a multidisciplinary approach that combines principles and practices from the fields of mathematics, statistics, artificial intelligence, and computer engineering to analyze large amounts of data.
This analysis helps data scientists to ask and answer questions like what happened, why it happened, what will happen, and what can be done with the results.
The accelerating volume of data sources, and subsequently data, has made data science is one of the fastest growing field across every industry.
Data science is a deep study of the massive amount of data, which involves extracting meaningful insights from raw, structured, and unstructured data that is processed using the scientific method, different technologies, and algorithms.
History of Data Science:
While the term data science is not new, the meanings and connotations have changed over time. The word first appeared in the ’60s as an alternative name for statistics. In the late ’90s, computer science professionals formalized the term.
A proposed definition for data science saw it as a separate field with three aspects: data design, collection, and analysis. It still took another decade for the term to be used outside of academia.
Data Science Used:
Descriptive Analysis-It helps in accurately displaying data points for patterns that may appear that satisfy all of the data’s requirements. In other words, it involves organizing, ordering, and manipulating data to produce information that is insightful about the supplied data.
Predictive Analysis-Predictive analysis uses historical data to make accurate forecasts about data patterns that may occur in the future. It is characterized by techniques such as machine learning, forecasting, pattern matching, and predictive modeling. In each of these techniques, computers are trained to reverse engineer causality connections in the data.
Diagnostic Analysis-It is an in-depth examination to understand why something happened. Techniques like drill-down, data discovery, data mining, and correlations are used to describe it. Multiple data operations and transformations may be performed on a given data set to discover unique patterns in each of these techniques.
Prescriptive Analysis-Prescriptive analytics takes predictive data to the next level. It not only predicts what is likely to happen but also suggests an optimum response to that outcome. It can analyze the potential implications of different choices and recommend the best course of action. It uses graph analysis, simulation, complex event processing, neural networks, and recommendation engines from machine learning.
List of Different Types of Data Scientists:
1.Machine Learning Scientists-Machine learning scientists aim to exploring new innovative approaches and examining new algorithms. They create such algorithms that are accustomed to suggest pricing strategies, products, derive patterns from large data inputs and demand forecasting.
2.Data Scientist-The increasing usage of GPS systems has given rise to a separate category of data scientists – spatial engineers. Google maps, bing maps, car navigation systems, and a number of applications, make use of spatial data for navigation, localization, site selection, etc.
3.Data engineer-A data engineer works with massive amount of data and responsible for building and maintaining the data architecture of a data science project. Data engineer also works for the creation of data set processes used in modeling, mining, acquisition, and verification.
4.Statistician-Statistician deals in both theoretical and applied statistics aiming towards business goals. Statisticians possess some of the key skills such as confidence intervals and data visualization, which can be inferred to acquire expertise in particular data scientist fields.
5.Mathematician-Mathematicians have been earning more acceptance into the corporate world due to their profound knowledge of applied mathematics and operational research. Their divine services are desirable by businesses to execute optimization and analytics in several fields, such as inventory management, supply chain, pricing algorithms, etc.
6.Business Analytic Practitioners-Business analysis is an art as well as science, and one cannot furnish to be led by either business acumen or by profound knowledge obtained based on data analysis. Business analytic professionals work on important decision-making processes like dashboard design, ROI. Analysis, high-level database design, ROI. Optimization, etc.
7.Quality Analyst-Quality Analyst has been connected with statistical process control in the manufacturing industry. This job has been advanced with modern analytic tools that are used by data scientists to prepare interactive visualizations serving as core inputs in decision making over groups like business, management, sales, and marketing.
How to become a data scientist:
There are usually three steps to becoming a data scientist:
- Earn a bachelor's degree in IT, computer science, math, physics, or another related field.
- Earn a master's degree in data science or related field.
- Gain experience in a field of interest
Data Scientist an IT Job:
Many people get confused about the nature of a data scientist’s job. As it’s an interdisciplinary role, people get confused about whether data scientist jobs would fall under the category of IT jobs or not.
A Data Scientist job is most definitely an IT-enabled job. Every IT professional is a domain expert responsible for handling a particular technical aspect of their organization.
Future of Data Science:
The future of data science is believed to witness some of the biggest innovations seen in the last decade, starting from the data explosion to the growth of the internet of things (IoT) and social media. Experts predict that in the next decade, the rise of machines with lead to the growth in usage and utility of computer systems and mobile devices.
Data scientists are likely to face a growing demand for their skills in the field of cybersecurity.
As the world becomes increasingly reliant on digital information, the need to protect this information from hackers and other cyber threats will become more important.
Difference between Data Science and Machine Learning:
|Data Science||Machine Learning|
|Data Science is a field about processes and systems to extract data from structured and semi-structured data.||Machine Learning is a field of study that gives computers the capability to learn without being explicitly programmed.|
|Need the entire analytics universe.||Combination of Machine and Data Science.|
|Branch that deals with data.||Machines utilize data science techniques to learn about the data.|
|It is a broad term for multiple disciplines.||It fits within data science.|
|Example: Netflix uses Data Science technology.||Example: Facebook uses Machine Learning technology.|
Benefits of Integrating Data Science And Artificial Intelligence:
Data science and artificial intelligence influence various aspects of society- from grocery shopping to commuting on public transport; everything is evolving since the integration of data science and AI.
Below, we enlist some of the benefits triggered by the integration of data science and artificial intelligence.
- Automation of intensive human tasks has helped the workforce to concentrate on other functions.
- Increases efficiency and productivity in Healthcare, Insurance, Pharma, Marketing, and other industries.
- Innovation systems to interact with customers and gauge their requirements.
- Forecasting disasters and preparing for them beforehand.
- It leads to a reduction in human errors.
Deep learning is a subset of machine learning that trains a computer to perform human-like tasks, such as speech recognition, image identification and prediction making.
It improves the ability to classify, recognize, detect and describe using data. The current interest in deep learning is due, in part, to the buzz surrounding artificial intelligence (AI).
Teams are successful using MATLAB for deep learning because it lets you:
1.Create and Visualize Models with Just a Few Lines of Code-MATLAB lets you build deep learning models with minimal code. With MATLAB, you can quickly import pretrained models and visualize and debug intermediate results as you adjust training parameters.
2.Perform Deep Learning Without Being an Expert-You can use MATLAB to learn and gain expertise in the area of deep learning. Most of us have never taken a course in deep learning. We have to learn on the job. MATLAB makes learning about this field practical and accessible.
3.Automate Ground Truth Labeling of Images and Video-MATLAB enables users to interactively label objects within images and can automate ground truth labeling within videos for training and testing deep learning models.
4.Integrate Deep Learning in a Single Workflow-MATLAB can unify multiple domains in a single workflow. With MATLAB, you can do your thinking and programming in one environment. It offers tools and functions for deep learning, and also for a range of domains that feed into deep learning algorithms, such as signal processing, computer vision, and data analytics.
Advantages and Disadvantages of Data Science:Advantages:
- Data Science helps organizations knowing how and when their products sell best and that’s why the products are delivered always to the right place and right time.
- Faster and better decisions are taken by the organization to improve efficiency and earn higher profits.
- It helps the marketing and sales team of organizations in understanding by refining and identifying the target audience.
- Extracted information from the structured as well as unstructured data for further use can also misused against a group of people of a country or some committee.
- Tool used for the data science and analytics are more expensive to use to obtain information. The tools are also more complex, so people have to learn how to use them.
Python Data Science:
Python is open source, interpreted, high level language and provides great approach for object-oriented programming. It is one of the best language used by data scientist for various data science projects/application.
Python provide great functionality to deal with mathematics, statistics and scientific function. It provides great libraries to deals with data science application.
One of the main reasons why Python is widely used in the scientific and research communities is because of its ease of use and simple syntax which makes it easy to adapt for people who do not have an engineering background. It is also more suited for quick prototyping.
Use Of Python in Data Science:
Thanks to Python's focus on simplicity and readability, it boasts a gradual and relatively low learning curve.
This ease of learning makes Python an ideal tool for beginning programmers.
Python offers programmers the advantage of using fewer lines of code to accomplish tasks than one needs when using older languages.
Pros and cons of Python for Data Science:Pros:
- Python is versatile, i.e. it is easy to use and fast to develop.
- It is open source and is blessed with a vibrant community.
- It is highly scalable.
- You can get all the libraries you can imagine in Python.
- It is great for prototypes. You can do more with less coding in this programming language.
- It is an interpreted language, hence you may find it a bit slower than some other programming languages.
- Due to the availability of GIL (Global Interpreter Lock), threading is not really good in Python.
- Python is not native to mobile environments. Some programmers also see it as a weak language for mobile computing.
- It has design restrictions.
- Some programmers also take python’s simplicity as its weakness. According to them, simplicity can offer you an easy start and a flat learning curve, but that can also affect your abilities to learn other complicated platforms.
Most Commonly used libraries for data science:
1.Numpy-Numpy is Python library that provides mathematical function to handle large dimension array. It provides various method/function for Array, Metrics, and linear algebra.
2.Pandas-Pandas is one of the most popular Python library for data manipulation and analysis. Pandas provide useful functions to manipulate large amount of structured data. Pandas provide easiest method to perform analysis. It provide large data structures and manipulating numerical tables and time series data.
There two data structures in Pandas:
I.Series-It Handle and store data in one-dimensional data.
II.DataFrame-It Handle and store Two dimensional data.
3.Matplotlib-Matplotlib is another useful Python library for Data Visualization. Descriptive analysis and visualizing data is very important for any organization. Matplotlib provides various method to Visualize data in more effective way.
4.Scipy-Scipy is another popular Python library for data science and scientific computing. Scipy provides great functionality to scientific mathematics and computing programming.
5.Scikit-Learn-Sklearn is Python library for machine learning. Sklearn provides various algorithms and functions that are used in machine learning. Sklearn is built on NumPy, SciPy, and matplotlib. Sklearn provides easy and simple tools for data mining and data analysis.