What is Data Science?
Data Science is a study which deals with the representation, identification, and extraction of information from the data source.
In other words, data science can be defined as the blend of various algorithms, tools, and machine learning principles. It is primarily used to make predictions, predictive casual analytics, pattern the discovery, and decision making.
Skills required for a Data Scientist:
There is no single way to define the data scientist role because of the data science itself is a wide-ranging field. People might be wondering about the role of a data scientist, so it is tough to get a simple meaning of data science.
The skills that are required for a data scientist are programming skills, analytical thinking, statistics measurement, and business acumen.
Many of the data scientists have a strong background both in mathematics, and other domains of science. Also, have the possibility of doing PhD in the relevant disciplines.
Anyone who is interested in building a career in data science should have the skills in three departments: Programming, analytics, and domain knowledge. Going deeper into one more step the following skills also help to become a righteous data scientist.
- Understand multiple analytical functions
- SQL database coding experience
- Ability to work from various sources like social media with unstructured data
- Strong knowledge of R, Scala, Python, SAS
- Knowledge of Machine Learning
Different types of Data Scientists:
In different organizations, the data scientists are allocated in different names. There are a number of software applications, and programming languages that support the analysts, and they also require many levels of programming skills.
The below are the different types of data scientists and their respective functions:
- Data Scientist as Statistician
- As Actuarial Scientist
- As Business Analytic Practitioners
- As Software Programming Analysts
1. Data Scientist as Statistician:
The statistics field is always about number crunching. A person can qualify to extrapolate interest in many data scientists fields.
Confidence intervals, data visualization, hypothesis testing, quantitative research, and Analysis of Variance (ANOVA) are the skills of a statistician.
Statistical knowledge with domain knowledge is the perfect combination for the work profile of statistician.
2. As Actuarial Scientist:
Many of the financial institutions and banks are depending on the actuarial scientists in order to predict the future revenue, income, and other marketing conditions by using mathematical algorithms.
A person may have the chance of becoming an actuarial scientist without knowing data science. But, the data scientist must have a grasp about statistical, and mathematical algorithms.
These algorithms are very much required for actuarial science. Chartered Financial Analyst (CFA) plays a crucial role in the work of actuarial science.
It is one of the important position in which data science professionals to apply statistical, and mathematical algorithms to financial services, banking, and insurance.
3. As Business Analytic Practitioners:
The final use of number crunching will be made by Business Analytic Practitioners (BAP).
Every BAP should have business acumen because the business analysis is an art as well as science and there is no chance of driving the capacity by either insight or by business acumen on data analysis.
Hence these professionals sit in between analytics(backend) and decision making teams(front end). The business analytic practitioners can work on important decision making like ROI optimization, metrics determination, ROI analysis, database design, dashboards design, etc.
4. As Software Programming Analysts:
This class of programmers have the skill of number crunching by using programming.
There are a number of programming languages like Python, Hadoop, R programming, Apache Hive all these languages are used to support data visualizations and data analytics.
The software program analyst also required to handle associated ETL and database tools that can extract and transform data by applying loads and business logic.
What is machine learning?
Machine learning is defined as the category of an algorithm that predicts the outcomes by allowing software applications without being explicitly programmed. Machine learning can also be used to build algorithms that can use statistical analysis and receive input data.
The machine learning processes are very similar to that of predictive modelling and data mining. Both search through data to look for adjusting program actions and patterns accordingly.
Most of the people are familiar with machine learning from internet shopping and also being served advertisements related to their purchase.
This happens because machine learning can be used by recommendation engines to personalize online advertisement delivery in real time.
Other than the personalized marketing, some other machine learning use spam filtering, network security threat detection, fraud detection, building news feeds and predictive maintenance.
How does machine learning work?
The algorithms of machine learning are often categorized as unsupervised or supervised. Unsupervised learning is trying to spot similarities about the data that splits into categories. It only consists of input data(X), but not corresponding output variables.
The main purpose of unsupervised learning is to model the structure or to check the distribution in the data. The term unsupervised learning comes because it doesn’t require any teacher and there is no correct answer to the questions.
The algorithms are in the own devices in order to present and discover the interesting data structure.
The problems of unsupervised learning can be categorized into clustering and association problems.
- Clustering: The problem where inherent groupings are discovered in the data is termed as a clustering problem. Ex: purchasing behaviour of grouping customers.
- Association: The association problem occurs when discovering rules for the data. Ex: If people want to buy ‘X’, but they also tend to buy ‘Y”.
Coming to the supervised learning it is the most popular learning system used by practical machine learning.
The supervised learning consists of both input(X) and output(Y) variables. An algorithm is also used to connect between input and output.
The main aim of supervised data is to map the functions to predict the value of the output(Y) by using the value of the input(X).
The term unsupervised learning comes because the process can be thought by the teacher and also supervising the data.
Here, we know the correct answers. The algorithm is used to make predictions on the output value and teacher corrects to get the appropriate answer. If the acceptable performance achieves then algorithm stops learning.
Skills Required to Become a Machine Learning Expert:
Machine learning provides the computers to perform some tasks like planning, recognition, prediction, diagnosis, robot control, etc., It can be used as a boost up the algorithm development and it teaches themselves to improve and mould when new data exists.
A candidate can have the opportunity to become a machine learning expert who is good at applied math, probability, problem-solving skills, statistics, analytical skills, understanding a broad set of algorithms. Apart from the skills, the candidate should have the curiosity to learn.
Here are some key skills for a machine learning expert:
- Probability and Statistics
- Distributed Computing
- Advanced Signal Processing techniques
- Applied Math and Algorithms
- Expanding the Expertise in Unix Tools
Probability and Statistics:
Candidate can learn algorithms with the help of theories. Great samples are Hidden Markov Models, Gaussian Mixture and Naive Bayes. In order to understand the models, we need to know about the probability and statistics. In this, the statistics are confusion matrices, p-values, operator-receiver curves, etc.
Nowadays, the machine learning jobs are working with large data sets. We have to use multiple machines to process this data. Using a single machine leads to a problem in the cluster.
So, we have to distribute it all over the cluster. Some of the projects like cloud services(Amazon’s EC2) and Apache Hadoop make this process cost-effective.
Advanced Signal Processing techniques:
Feature extraction plays a crucial role in machine learning. For solving many problems we have to utilize the signal processing techniques. Shearlets, bandlets, wavelets, and contourlets are some of the advanced signal processing techniques.
The operator should know the time-frequency analysis and also how to implement it. Some other advanced analysis like convolution and Fourier analysis should also learn by the operator.
If someone wants to build a career in machine learning he/she should learn some programming languages like Python, R, C++, and Java.
Because C++ is used to speed up the code, R-language greatly works on plots and statistics, Hadoop is on the Java-based and so on these programming languages are very helpful for the candidate to build his career in machine learning.
Applied Math and Algorithms:
In order to know how an algorithm works and how to understand algorithm theory, we have to discriminate SVMs models.
The candidate needs to understand and concentrate on some subjects such as convex optimization, quadratic programming, gradient descent, partial differential equations, Lagrange and alike.
Expanding the Expertise in Unix Tools:
We should also learn and master the all great Unix tools like awk, grep, cut, cat, tr, sort and so on. Most of the processing is on the Linux-based machine the candidate should need access for these tools.
Also, he/she should learn their utilization and functions. These tools make the machine learning so easier.
Data science is referred to as actionable insights from data. Whereas machine learning is one of the braches in artificial intelligence and is very much useful for data science.
Both are known as the emerging fields improving into different fields which helps in improving the performance and efficiency of the company.
They are going to the celestial rate. These two fields have its own use, but both the data science and machine learning are based on the data gathering and analyzation.