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Category: Analytics

Would a master’s degree be required for a career in Big Data analytics or Data Analytics?

Posted on by Imarticus Learning

 

Run any search for jobs in Business Analytics, and you find there are ample opportunities for aspirants with zero experience. All they ask for is skills in business and data analytics.

Here are the skills required to succeed in business analytics career. 

  1. An Inquisitive mind:
     A keen solution finder uses a large data set to understand the how’s and why’s of any complex financial situation and works to find the correct solutions. This trait is absolutely essential for the role of a BA.

  2. Communication and perceptive skills
     Actionable solutions, domain expertise, and experience in interpreting the solutions are essential when designing a service or product, making decisions and working on large volumes of data. To form the right strategy, one needs great perceptive and communicative skills to implement the solutions which are data driven.

  3. Conceptual understanding of methodology and tools
     Knowledge of Excel, SAS etc. is a good starting point. However, applying these thoughts to data analysis with them will take you places. These are vital skills for every BA coupled efficiently with intelligence to use the results practically.

  4. Observing small details
     Keeping the small details in the big picture meticulously in mind ensures correct perceptions and insights.

  5. Numerical and Quantitative Skills
     Figures, mathematical formula and Big Data will be constant companions. To make a successful career, you will find that the BA job roles call for a fusion of project management and analytics. A foundation in engineering and mathematics with excellent communication and analytical skills is essential.

    While engineers tend to move towards the information management and data engineering fields, aspirants with some business experience easily transform into roles as a Business Analyst. An MBA graduate should enhance skills by doing a business analytics course in Thane.
  6. Those graduates with an MBA who already have some background in business should opt to enable a career as a BA by upgrading skills with online short courses. Such aspirants will be empowered with a good skill set in business analytics that helps start a career with some certification.

    Requisite technical skills    : A Business Analyst must have proficiency in the application of statistics with conceptual knowledge of suites like SQL, R, SAS, testing framework, SPSS, Hive and tools in BI such as Tableau, Excel, Spotfire, Qlik, among others. Skill sets required change depending on the infrastructure and organisation’s requirements and functional roles needed.

    These courses offer a good grasp of fundamentals, concepts, theoretical knowledge, practical skills and certifications that could help enhance your resume and career. They also offer boot camps, short term workshops, and basic knowledge of SAS and R. 

    While certification definitely helps you need to be an excellent communicator and work diligently to acquire the best analytical and business skills. Another advantage in such courses is of mentoring by certified and experienced industry aces that helps garner the latest best practices, techniques, skills, and practice on the latest trending technologies in the field of Business Analytics.

    Payouts: BAs bring analytic and business skills to the table and receive good remuneration. The average annual salary is 859,025 INR/ year for a Senior Business Analyst as per the figures of Pay scale. The annual average payouts of a BA is INR 6,44857/year as per Glassdoor. 

    Top Employing Enterprises: Some high-ranking companies in Business Analytics to watch for are Cognizant, TCS, IBM, Wipro, Infosys, Accenture, HP, Deloitte, Capgemini, Genpact among others and in no particular order. Some startups like Mu Sigma Analytics, Fractal Analytics, AbsolutData can offer you the best opportunities in the field of business analytics.

    Though these technical skills will definitely help you for a career in Big data analytics, still it is a beneficial if you have master’s degree, which will also boost your career in business analytics.

How Can You Start Learning Data Science and Become a Master in it?

Posted on by Imarticus Learning

 

Being a new and fast-growing field, Data Science is in desperate need of skilled individuals. With lucrative opportunities and pay scales, enterprises around the globe have been in search of skilful professionals to work for them.

You too can make use of this possibility and have a career of your dreams. But becoming a data scientist isn’t an overnight thing. It takes time and effort. So, How do we start to learn data science at right foot? We will find out.

Following are the few steps you could follow to learn data science.

  1. Find If Its Right for You
    Before fixing on to this career choice, you have to make sure you are totally interested in this. You can ask following questions yourself to find if its right for you.
    • Do you really enjoy programming and statistics?
    • Are you willing to work in a field where you have to learn about the new techniques and technologies constantly?
    • Are you okay with job titles like Data Analyst, business analyst etc. ?
    If you have yes for an answer, then you can start learning Data Science right away.
  2. Mathematics
    You have to get familiar with a few topics in Maths in order to conquer data science. The main topic you need to study are the following
    • Probability – A lot of data science works are based attempting to measure the probability of events. Textbooks are a good source of information for this subject.
    • Statistics – This branch of mathematics deals with interpreting and analyzing the data. Fortunately, great textbooks are available online for you to refer.
    • Linear Algebra – This branch of maths covers the study of vector spaces and linear mapping among this space. Linear algebra is a must to understand how machine learning algorithms work.
    Once you are familiar with programming and various libraries, you may not have to dive deep into these mathematical details. But to understand them properly, you will need a sound base in these mathematical topics.
  3. The Programming
    Data Science community has chosen Python and R as their primary languages for programming due to various advantages. You have to learn and practice programming in these two languages at least for the following topics.
    • Data Analysis – NumPy and Pandas, are the two common libraries used for data analysis in Python. Tidyverse is a popular compilation of packages in R for data analysis.
    • Data Visualization – Matplotlib is the most used data visualization tool in Python. The most popular plotting library in R is ggplot2.
    • Machine Learning – Python mostly make use of SciKit-learn library to do the machine learning works. When it comes to R, it offers a huge variety of packages including CARET, PARTY, random forest and many other.
    When you complete these steps, you have a solid base required for a Data Scientist. Even if you find it hard to learn all this stuff on your own, the courses on data science prodegree by Imarticus is available to help you master the Data Science. The course provides comprehensive coverage of statistics and data science along with hands-on training on the leading analytical tools. so, stop wasting your time start preparing for your data science career right away.

How Do You Start Learning Data Analytics Step by Step?

Posted on by Imarticus Learning

If you are looking to make a career in data analytics congratulations! You just found the one niche where the demand outstrips supply and employers find it hard to find the right candidate. The areas where demand for BAs outstrips the supply are retail, healthcare, banking, hospitality, e-commerce, and manufacturing amid many others.

What the payouts are: BAs ring analytic and business skills to the table and receive good remuneration. The average annual salary is 859,025 INR/ year for a Senior Business Analyst as per the figures of Pay scale. The annual average payouts of a BA is INR 6,44857/year as per Glassdoor.

Demand for jobs: Some high-ranking companies in Business Analytics to watch for are Cognizant, TCS, IBM, Wipro, Infosys, Accenture, HP, Deloitte, Capgemini, Genpact among others and in no particular order. Some startups like Mu Sigma Analytics, Fractal Analytics, AbsolutData can offer you the best opportunities in the field of business analytics.

Top roles are:

  • Data Analyst
  • Business Analyst
  • Product Manager
  • Digital Marketer
  • Quantitative Analyst

So let us look at how you would go about learning and equipping yourself best for this job.

How can you pursue a career in business analytics?
Business analytics helps companies with gainful insights from large volumes of data it generates to automate and tweak business processes. Today, Big Data is the largest asset of companies which they deploy through statistical analysis to perform predictive business analytics, find relationships, patterns, and understand future outcomes.
Here is how you can jump-start a career and learn business analytics.

There is constant demand in modern times for business analysts and MBA qualified candidates in the developing ITES and IT industries.

Begin your career with a business analytics course in India.
On the road to a bright career today, you find that the BA job roles call for a fusion of project management and analytics. A foundation in engineering and mathematics with excellent communication and analytical skills is essential. However, those who already have some background in business opt to enable a career as a BA by upgrading skills with online short courses.

These aspirants are empowered with a good skill set in business analytics that helps them start a career with some certification. Graduates in engineering tend to move towards the information management and data engineering fields, while aspirants with some business experience easily transform into roles as a Business Analyst. An MBA graduate should enhance skills by doing a business analytics course.

Learning the Requisite Skills: A Business Analyst must have proficiency in the application of statistics with conceptual knowledge of suites like   SQL, R, SAS, testing framework, SPSS, Hive and tools in BI such as Tableau, Excel, Spotfire, Qlik, among others. Skill sets required change depending on the infrastructure and organization’s requirements and functional roles needed.

These courses offer a good grasp of fundamentals, concepts, theoretical knowledge, practical skills and certifications that could help enhance your resume and career. They also offer boot camps, short term workshops, and basic knowledge of SAS and R. While certification definitely helps you need to be an excellent communicator and work diligently to acquire the best analytical and business skills.

Another advantage in such courses is of mentoring by certified and experienced industry aces that helps garner the latest best practices, techniques, skills, and practice on the latest trending technologies in the field of Business Analytics.

Convince yourself with the right inferences and answers from the data above and start off on a promising journey in business analytics.

We offer data analytics courses at our centers in Mumbai, Thane, Pune, Jaipur, Delhi, Gurgaon, Bangalore, Chennai, Hyderabad, Coimbatore.

How can You Pursue a Career in Business Analytics?

Posted on by Imarticus Learning

Business analytics helps companies with gainful insights from large volumes of data it generates to automate and tweak business processes. Today, Big Data is the largest asset of companies which they deploy through statistical analysis to perform predictive business analytics, find relationships, patterns, and understand future outcomes.
The roles and functions of BI and BA are used in conjunction with the key differentiators being that BA deals with statistical analysis, data mining, multivariate testing, and predictive modeling. A career in business analytics is today the preferred and popular choice in ITES and IT sectors. The BA with an analysis in real-time supports decision making works closely with peers, and the organizational management to aid matters of marketing, product development and so on through data-driven decisions.
Here is how you can jump-start a career in business analytics. There is booming constant demand in modern times for business analysts and MBA qualified candidates in the developing ITES and IT industries. The areas where demand for BAs outstrips the supply are retail, healthcare, banking, hospitality, e-commerce, and manufacturing amid many others.
Jump start your career  
On the road to a bright career today, you find that the BA job roles call for a fusion of project management and analytics. A foundation in engineering and mathematics with excellent communication and analytical skills is essential.
However, those who already have some background in business opt to enable a career as a BA by upgrading skills with online short courses. These aspirants are empowered with a good skill set in business analytics that helps them start a career with some certification.
Requisite Skills: A Business Analyst must have proficiency in the application of statistics with conceptual knowledge of suites like   SQL, R, SAS, testing framework, SPSS, Hive and tools in BI such as Tableau, Excel, Spotfire, Qlik, among others. Skill sets required change depending on the infrastructure and organization’s requirements and functional roles needed.
While engineers tend to move towards the information management and data engineering fields, aspirants with some business experience easily transform into roles as a Business Analyst. An MBA  graduate should enhance skills by doing a business analytics course in Mumbai.
These courses offer a good grasp of fundamentals, concepts, theoretical knowledge, practical skills and certifications that could help enhance your resume and career. They also offer boot camps, short term workshops and basic knowledge of SAS and R. While certification definitely helps you need to be an excellent communicator and work diligently to acquire the best analytical and business skills. Another advantage in such courses is of mentoring by certified and experienced industry aces that helps garner the latest best practices, techniques, skills, and practice on the latest trending technologies in the field of Business Analytics.
Payouts: BAs bring analytic and business skills to the table and receive good remuneration. The average annual salary is  859,025 INR/ year for a  Senior Business Analyst as per the figures of Payscale. The annual average payouts of a BA is INR 6,44857/year as per Glassdoor.
Top Employing Enterprises: Some high-ranking companies in Business Analytics to watch for are Cognizant, TCS, IBM, Wipro, Infosys, Accenture, HP, Deloitte, Capgemini, Genpact among others and in no particular order. Some startups like Mu Sigma Analytics, Fractal Analytics, AbsolutData can offer you the best opportunities in the field of business analytics.

How Can You Make a Good Career in the Data Analytics Industry? What are the Skills You Need to Develop If You Have to Start From Scratch?

Posted on by Imarticus Learning

Careers don’t just happen. Especially those in Big Data and Analytics! Let us look at the needed skills and what you need to do to make a happening career in this field. Here are the basic steps in the path to success.
Do the math:
Your game plan and strategy counts! Firstly, research your thoughts, options and why you want a career in this field, what will the payouts be, what is the scope for the job roles you aspire for, which are the top companies, how you plan to put your plan into action and have a great SWOT analysis. Some relevant information here may help.
Some high-ranking companies in Business Analytics to watch for are Cognizant, TCS, IBM, Wipro, Infosys, Accenture, HP, Deloitte, Capgemini, Genpact among others and in no particular order. Some startups like Mu Sigma Analytics, Fractal Analytics, AbsolutData can offer you the best opportunities in the field of business analytics.
Top roles are

  • Data Analyst
  • Business Analyst
  • Product Manager
  • Digital Marketer
  • Quantitative Analyst

BAs bring analytic and business skills to the table and receive good remuneration. The average annual salary is 859,025 INR/ year for a Senior Business Analyst as per the figures of Payscale. The annual average payouts of a BA is INR 6,44857/year as per Glassdoor.
Plan your career strategy:
BA job roles call for a fusion of project management and analytics. A foundation in engineering and mathematics with excellent communication and analytical skills is essential. However, those who already have some background in business opt to enable a career as a BA by upgrading skills with online short courses and a career in data analytics courses. 
These aspirants are empowered with a good skill set in business analytics that helps them start a career with some certification. Graduates in engineering tend to move towards the information management and data engineering fields, while aspirants with some business experience easily transform into roles as a Business Analyst. An MBA graduate should enhance skills by doing a business analytics course.
Plan acquiring your skill set:
Here’s a list of technical skills required. A Business Analyst must have proficiency in the application of statistics with conceptual knowledge of suites like   SQL, R, SAS, testing framework, SPSS, Hive and tools in BI such as Tableau, Excel, Spotfire, Qlik, among others. Skill sets required change depending on the infrastructure and organization’s requirements and functional roles needed.
Do a course to acquire them:
The business analytics course in Mumbai offers a good grasp of fundamentals, concepts, theoretical knowledge, practical skills and certifications that could help enhance your resume and career. They also offer boot camps, short term workshops, and basic knowledge of SAS and R. While certification definitely helps you need to be an excellent communicator and work diligently to acquire the best analytical and business skills. Another advantage in such courses is of mentoring by certified and experienced industry aces that helps garner the latest best practices, techniques, skills, and practice on the latest trending technologies in the field of Business Analytics.
Apply what you learn:
Knowledge implies having the ability to translate theory into action. Accumulated skills get rusty in a while. So continue to hit the refresh button on your skills and do relevant courses in garnering additional skills that will set you apart from the aspiring job queues.
Get some experience:
Apply for internships to get some valuable experience on your resume. You can also volunteer and work part-time for some of the larger companies to ensure you have practical skills.
Now that you possess most of the skills required, do an internship and land a job based solely on your skills.

Are Data Scientists Useful at Pharmaceutical Companies?

Posted on by Imarticus Learning

Data science has been disrupting the industries for the past couple of years. With the advent of technology, data and the hidden insights in them are widely being used to improve every industry. The industries like finance and health care have already made their way up using data science technology. This article discusses whether data scientists are important to pharmaceutical companies.
Well, data in the hands of a skillful data scientist have great value. The data in Pharmaceutical companies are no different. With the aid of a data scientist, these companies hold great opportunities to improve their operations. How exactly do they help? Keep reading to find out.
The Drug Development
Unlike many challenging problems resolved by engineers, drug development has no overall model serving as a basis for optimization. This multidisciplinary and complicated process is in great due for a significant improvement. Pharmaceutical companies nowadays are generating great amounts of data, driven mostly by the sequencing of human genomes. These companies are now realizing the importance of data scientists in developing algorithms that can uncover efficiencies by analyzing this data. Since the time of its inception, pharmaceutical companies are striving to meet the new clinical needs without affecting their financial health. The data science approach is expected to play a critical role in meeting this requirement.
Clinical Trial Planning
The phase III clinical trials have always been a headache for the pharmaceutical companies, especially in the light that the patent exclusivity of a drug starts roughly around the time of its first clinical trial. The practical complications with recruiting and randomizing a sufficient number of patients result in increased costs and delays. It also erodes the time over which the company can recoup the costs during the period of patent exclusivity. Data-driven approaches are now taking over this issue. They are now resorting to data science to build precision into the way they calculate the feasibility of successful clinical trials within the time and expense constraints.
Drug Repurposing
Drug repurposing is an attempt to find an altogether different use for a drug. It can be attempted on both drugs that are on the market and the ones stopped developing. It is usually done by evaluating a hypothesis put together by a scientist in a laboratory. It eliminates the time and cost incorporated with developing a medicine. With the recent update brought by data science, this discovery can be made quicker by computation of complementary “drug-disease” pair on large public repositories of sequencing and gene expression data. It will ultimately result in cheaper medicines.
Wearables 
Wearables technology provides a method of unobtrusively capturing continuous physical measurements. They are aimed at replacing the expensive instruments that are traditionally used. With the aid of data science, pharmaceutical companies are aiming to solve the problem in medication adhering in clinical trials.  So, it is clear that pharmaceutical companies are also in need of trained data scientists.  You can start your journey to a successful data science career by taking the data science prodegree by Imarticus Learning. It provides with all the necessary skills required for your career. It is one of the best data science course in Mumbai.

What is the Artificial Intelligence Markup Language?

Posted on by Imarticus Learning

Artificial intelligence is the technology of the future. It has exploded onto the world ever since it was first developed, and the technology has since been implemented in a lot of fields, ranging from healthcare to warfare. AI looks all set to stay and is sure to play a huge role in how the future of humanity is shaped.

However, it should be noted that AI was not always developed using popular languages today. Currently, Python and R represent the most popular languages which are used in machine learning and consequently, in AI too. However, there are a lot of other languages and methods which were used at times to various ends.

AIML was one such language which was used in the development of early chatbots. Digital assistants or chatbots truly represent the dawn of a new chapter in the scientific advancements of humankind. Chatbots are now increasingly becoming a part of most companies, and most of the internet users have already interacted with a chatbot in some form or other.

Being an AI aficionado or a prospective practitioner, you can surely try to build a chatbot from scratch in order to gain some practice in Artificial Intelligence.

What is AIML?
Artificial Intelligence Markup Language or AIML was created by Dr Richard Wallace and is currently offered as an open source framework for developing chatbots. It is offered by the ALICE AI Foundation so that users can create intelligent chatbots for their use from scratch.

AIML is an extremely simple XML, just like HyperText Markup Language or HTML. It contains a lot of standard tags and tags which are extensible, which you use in order to mark the text so that the interpreter which runs in the background understands the text you have scripted.

If you want the chatbot to be intelligent, it is important to have a content interface through which you can chat. Just like XML functions, AIML also characterizes rules for patterns, and decide how to respond to the user accordingly. AIML has several elements in them, including categories, patterns, and templates.

Categories are the fundamental units of knowledge which are used by the AIML and is further divided into the two other elements mentioned above – templates and patterns. In layman’s terms, patterns represent the questions asked by the user to the chatbot, or what the chatbot perceives as questions which need to be responded to.

The templates are the answers which it remembers based on its training, and which are subsequently modified and presented as replies to the users. Template elements basically include text formatting for the responses, conditional responses taught to it including many if/else scenarios and random responses which always come in handy while interacting with a user.

AIML is now open source, and users can start to create a chatbot by learning the fundamentals of the language. If you find yourself yearning to know more about this and AI in general, you should check out the many artificial intelligence courses on offer at Imarticus Learning.

What is the Best Programming Language For Artificial Intelligence Projects?

Posted on by Imarticus Learning

Artificial Intelligence is the hot topic of the last couple of years and is all set to be the science of the future. It has already opened up a realm of possibilities for humans, and by taking advantage of a machine and deep learning, it is no doubt going to play a huge role in the future of humanity. You can do almost anything with this technology – even build apps which can hear, see and react accordingly.

A lot of newcomers are beginning to get into programming for AI, considering how important it is turning out to be. However, with the plethora of options available, it can be difficult to choose a particular language for programming. Let us consider the many languages which are currently being used for AI development.

Python
Currently rising in popularity, it is one of the main languages which come up in how to learn machine learning. Being extremely simple to use and learn, it is preferred by many beginners. Compared to other languages like C and Java, it takes extremely less time for implementation.

Another advantage is that with Python, you can opt for procedural, objective oriented or functional style of programming. There are also a lot of libraries which exist for Python, which make programming considerably easier.

Java
A comparatively older option, it first emerged in 1995 – however, it’s importance has only grown at an unparalleled rate since then. Highly portable, transparent and maintainable, this language also has a large number of libraries to make it easier for the user.

Java is incredibly user-friendly and easy to troubleshoot and debug, and the user can also write code that runs on different platforms with ease. The Virtual Machine Technology implemented in Java is key to this feature, actually. Many Big Data platforms like Apache Spark and Hadoop can be accessed using Java, making it a great all-around option for you.
Julia
Developed by MIT, this language is meant for mathematical analysis and numerical computing to be done in a high-performance fashion. These features make it an amazing choice for AI projects since it was designed keeping the needs of Artificial Intelligence in mind. Separate compilation is done away with, too – however, it is only growing, so it does not have the same number of libraries as the others.

Haskell
Haskell, unlike Java, is a great choice for engaging and working with abstract mathematical concepts. You can create AI algorithms using the expressive and efficient libraries which come with the language, and the language is far more expressive compared to many others.

Probabilistic programming is also a cakewalk since developers are able to identify errors relatively quickly, even during the compile phase of iteration. However, you still cannot expect the same level of support that Java and Python offers.

You will need to learn some machine learning skills, if you are to have a long career in this field – in order to do that, you should check out the big data and machine learning courses on offer at Imarticus Learning.

What are The Different Fields in Data Analytics?

Posted on by Imarticus Learning

One of the most popular technology-empowered jobs out there, data analytics consists of various disciplines in the field of data science. There are plenty of different areas in which data analytics is applied, with the banking sector being the foremost. As the world starts adopting data analytics techniques, there are different jobs that are present in the field of data analytics.

Here are four of the main fields in the data analytics sector:
1.Data analyst:
Some companies use the terms “data scientist” and “data analyst” interchangeably. Data analysts generally work with SQL databases and pull data out of the same. The job also entails becoming a master of Tableau and Excel and occasionally analyze results of A/B testing and leading the Google Analytics account. Other roles can also include reporting dashboard data and producing data visualizations.
2.Data Engineer:
Data engineers are generally bought in when companies start getting a lot of traffic and need someone to set up the infrastructure to move forward. There’s also a need for somebody to provide constant analysis and this job can generally be posted under “Data Scientists” or “Data Engineers” as well.
Data engineers require a decent knowledge of machine learning, and heavy statistics as these are one of the main assets companies look for when they’re starting out themselves. Software engineering skills are seen as more of a secondary requirement during the initial phase. Data engineers generally get to own all their work but won’t have much guidance and could reach a point of stagnation.
3.Machine Learning Engineer:
There are many companies where data ends up being their main product. Data analysts or machine learning will be a huge part of their internal processes here. A machine learning engineer who has an education in statistics, physics or mathematics will have a bigger role in these situations. If they’re looking at continuing in an academic path even afterward, then this is a great role to fulfill.
Most companies which look out for machine learning engineers are consumer-facing and have huge data which they offer out to other companies.
4.Data science generalist:
Companies look for data science generalists to join other data scientists internally. Companies that take interview care about data but aren’t necessarily a data company themselves. They will be on the lookout for individuals who can work on a wide variety of hats, including touch production code, analysis, data visualization and more.
Data science generalists are sought after to fulfill any specific niche which a company feels their team lacks. This can include areas such as machine learning or data visualization for example.
Thus, it’s important that you’re always on the lookout for a job that satisfied your skill set the best. There are so many options available for those interested, and with data analytics shaping the world we live in, it will serve you well if you can find your own niche.
Join Imarticus to get the best in big data analytics courses and fast forward your career graph in the field of data science. We offer data analytics at our centers in Thane, Pune, Bangalore, Chennai, Hyderabad, Coimbatore, Delhi.

What are The Best Machine Learning Prediction Models for Stocks?

Posted on by Imarticus Learning

Predicting stock prices has been at the focus for a long time due to monetary benefits it can yield. Prediction of the future stock price is trying to determine the future value of a company stock which is traded on a stock exchange. Traditionally investors have relied upon fundamental research and technical analysis to predict the stock price movements.  Fundamental analysis is concerned with the performance of the company and its business environment. Investors mainly consider the current price and likely future performance of the company while picking the stocks.
Technical Analysis is concerned with past patterns of the stock price movements and predicting future trends. Lately,  machine learning models are also used in technical analysis to process the historical and current data of public companies to predict their stock prices. Mathematical models can be developed which process historical data about quarterly financials, trading data, latest announcements, and news flow etc and machine learning techniques can identify patterns and insights that can be used to make predictions for stocks. Trading signals can be generated and because correlation based on which the trading call is given is often weak, the time window in which profit can be made by the execution of the trade is usually very small.  Therefore, firms that specialize in ‘quant’ trading keep their machine learning algorithms simple and secretive so their trading strategies can be optimized for speed and reliability.
Now, we take a brief look at some of the machine learning models for prediction of stock prices.
Moving Average – Moving average is average of past ‘n’ values and is considered widely in technical analysis.  20 day, 50 days and 200-day moving averages of stock prices and indices are critical data points in predicting future trends.
Exponential Moving Average (EMA) differs from simple moving average in that it gives greater weightage to the most recent values compared to the older values.
Linear Regression is another commonly used statistical approach to model the relationship between a scalar response and one or more independent variables.
Support Vector Machines (SVM) is a machine learning technique based on binary classification, which is now greatly used in predicting whether the price of a stock will be higher or lower after a specific amount of time-based on certain parameters.
There are also a few non-statistical models that are being used to forecast stock price movements. A textual analysis of financial news articles is one such method. In this method, a crawler is trained to scan all the financial news articles and look for the patterns that are likely to have an impact on prices of specific stocks. Text mining of historical news articles with concurrent time series analysis can be done to figure out the impact of various types of news articles. Different weightage for articles based on the credibility of their sources can be given.
Thus, Machine learning can be applied to stock data and mathematical models can be developed to predict stock prices. Trading strategies can be optimized for speed relying on these models while simultaneously eliminating human sentiments from decision making.
There is a lot to explore with regards to stock predictions and machine learning models that need further explanation cannot be expatiated in a concise article like this.  The machine learning future in India is very bright.  If you need to pursue machine learning courses, learn from pioneers like Imarticus.