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Category: Analytics

6 Skills to Become a Business Analyst

Posted on by Imarticus Learning

The business analyst position is one of the most promising career options in the age of data science. Any company that relies on progressive technology uses tons of data to make operational decisions that’ll be profitable for the business. The internet is flooded with business analyst courses given the widespread demand for business analyst professionals by businesses. So what does a data analyst do? Well, from a broader perspective the role involves churning tons of data to analyse the industry trend and market situation and make suggestions regarding the business operations. Let’s delve deeper into what the role encompasses and what are the key skills required for the job.

Role of a Business Analyst

The role of a business analyst is very dynamic and holds an array of responsibilities. A business analyst is responsible to bridge the gap between the IT and non-IT departments using data analytics. They work in close conjunction with the IT and Finance department to build a data-backed model which will help to improve business processes and minimize the cost incurred on such expeditions.
A typical job description for a business analyst might include tasks like budgeting and forecasting, price optimisation, variance analysis, doing a SWOT analysis, identifying new profitable avenues for the business, communicating with key stakeholders for a comprehensive analysis, etc. The job is multidimensional and requires very skilled professionals who have honed both their soft skills and technical skills to perfection. Let’s peep into some of the key skills required by business analyst professionals in the field.

Skills Required To Become Business Analyst

The job of a business analyst has multiple aspects to it and requires a mixture of balanced technical and soft skills, let’ learn about some of the key skills that help the business analyst in their professional life.

  • Technical skills: Technical skills are the most crucial ones as a lot depends on the  competency of a business analyst to carry out details business analysis by factoring in multiple variables that might affect the business. Most of the software developments take place only after the business analyst’s recommendations post analysis. Understanding the technical applications and its relevance to the business is utmost important. In addition to this, a BA should also be able to analyse market trends and predict future trends based on historical data.
  • Research skills: Given the enormous amount of information present in the age of data science, it is imperative to find relevant contextual information that’ll help the business. Research skills are the key to finding reliable information that is relevant to a particular business or industry. Using data analytics on irrelevant data won’t get the best results for a corporation.
  • Critical thinking: Being good at research will not suffice. What you draw out from the research data is crucial to your success. This requires thinking critically with all that information present at hand after doing the research. When you obtain valuable insights from the data you can apply it to grow the business further. A lot of contemplation goes into drawing out conclusions from the data.
  • Communication skills: When you can’t express all that you have understood about the business, it won’t be easier for people around you to comprehend your thinking and motive. An effective communicator needs to put forth their own opinion so that other parties can understand the data-based insights obtained. Being a good communicator means you should be as good a listener as you are a speaker. Listening to various stakeholders is the key to effective communication.
  • Interpersonal skills: When you are working as a business analyst in a company, it requires you to communicate and understand the needs of various stakeholders. This requires you to have good interpersonal skills so that you can get along with the key stakeholders. The key here is to understand the motivation, objectives and inspirations of different parties to reach an understanding outcome.
  • Negotiation skills: When you have to manage multiple stakeholders to the business you won’t be able to satisfy all the parties. The best outcome for one party will not necessarily produce the same for the other party. This needs an effective negotiator to balance the scales and settle the difference. Managing the conflict of opinions is the key to negotiating here.

Best Data Science Institutes in India!

Posted on by Imarticus Learning

According to Glassdoor, Data Science was the highest-paid field to get into! The demand for data science is very high, while the supply is too low.

Have Some Questions? Explore more here!

How long does it take to get Tableau Certification?

With data becoming one of the core values of many organizations in the world, having tools that work best with data is the key. One such tool is Tableau. It is the fastest and powerful software used for data visualization. It simplifies raw data into a comprehensible format.

With a skill shortage in the field of Data Analytics, Tableau can help build a workforce of talented individuals who can contribute to the industry. For this, we need to understand how long it takes to get a Tableau certification.

There are three types of tableau certifications –

Tableau Desktop Certification, Tableau Server Certification, and Delta Exams.

Tableau Desktop Certification

In this, the certification levels are Tableau Desktop 10 Qualified Associate (2 hours), Tableau Desktop 10 Certified Professional (3 hours), and Tableau Desktop 10 Delta Exam (1 hour).

E-learning and Distance LearningTableau Server Certification

In this, there are certification levels which are Tableau Server 10 Qualified Associate (1.5 hours), Tableau Server 10 Certified Professional (7 hours), and Tableau Server 10 Delta Exam (1 hour).

The time taken depends on the certification level based on the qualification and experience in using Tableau, which may vary from 1 hour to 7 hours.

Which Institute Is The Best For Data Science In Mumbai?

Data science is the latest trend in many organizations that work with data every day, especially for analytics in order to boost their sales and recognize loopholes in their operations.

There are several renowned institutes that offer a Data science course in Mumbai. Some of them are NMIMS School of Business Management, ISME School of management and entrepreneurship, Imarticus Learning Private Limited, Aegis School of Business, Tata Institute of Social Sciences, and SP Jain Institute of Management and Research.

Imarticus Learning is a professional educational institute that focuses on bridging the gap between industries and academics. It builds powerful models and generates useful business insights and predictions for businesses.

The data science course at Imarticus helps the learners to gain job-relevant skills like R, Python, SQL, and Tableau, gain industry certification, experience 360-degree learning which comes with turbo-charged curriculum,  and hands-on experience showing real-life problems in the business world, and video case studies. The course involves expert inputs and certification endorsed by KPMG, a global leader in Artificial Intelligence and Data Science consultancy.

Which institute is the best for data science in Pune?

Data science is the latest trend in many organizations who work with data every day, especially for analytics in order to boost their sales and recognize loopholes in their operations.

There are several renowned institutes that offer Data Science training in Pune including Imarticus Learning Private Limited. Imarticus Learning is a professional educational institute that focuses on bridging the gap between industries and academics. It builds powerful models and generates useful business insights and predictions for businesses.

The data science course at Imarticus offers a broad exposure to key concepts and helps the learners to gain job-relevant skills like R, Python, SQL, and Tableau, gain industry certification, experience a 360-degree learning which comes with turbo-charged curriculum,  and hands-on experience showing real-life problems in business world, and video case studies.

The course involves expert inputs and certification endorsed by KPMG, a global leader in Artificial Intelligence and Data Science consultancy. The instructors and trainers will guide you from the beginning till the end of the course, and you can stay in touch with them and continue to follow up with useful guidance even after completion of the course.

Which is the best training institute for Data Science coaching in Ahmedabad?

Data science is the latest buzz in the organizations who deal with data on a daily basis, especially for data analytics or data science to boost their sales and recognize loopholes in their operations.

There are several renowned institutes that offer Data Science Classes in Ahmedabad including Imarticus Learning Private Limited.

Key to Inclusive Leadership

Imarticus Learning is a professional educational institute that focuses on bridging the gap between industries and academics. It builds powerful models and generates useful business insights and predictions for businesses.

The data science classes at Imarticus help the learners to gain job-relevant skills like R, Python, SQL, and Tableau, gain industry certification, experience a 360-degree learning which comes with turbo-charged curriculum,  and hands-on experience showing real-life problems in business world, and video case studies. The course involves expert inputs and certification endorsed by KPMG, a global leader in Artificial Intelligence and data science consultancy.

How to Become a Salesforce Business Analyst?

Posted on by Imarticus Learning

Who is the Salesforce Business Analyst?

Salesforce business analyst is the people who analyze the business process with technology. They can understand the formation of the organization. These analysts have the power to improve Salesforce maintenance. They are the people who bloom with information giving business understanding on taking various information sources. These analysts need to be very motivated and have a high concentration on identifying, arranging and value for Salesforce that is directly dependent on the essentials of the organization’s clients.

They are the people who decide what business should follow. Such an analyst takes care of the issues and makes the desired proposal. Salesforce business analyst has essential knowledge of the Salesforce platform to form an informative action plan that makes bigger projects into small scale projects.

They are the people who have extensible knowledge and firstly they decide to extract what problem needs to be solved and then find out the solution for it. The job of these analysts requires a lot of study work and cut down business procedures. The task is to train users of Salesforce and being the salesforce champion.

Being a salesforce business analyst, you would be responsible for delivering cloud technology solutions. Their main functional areas are salesforce configuration, testing, training, and development support. Being an analyst, you have to work strongly with solution architects and technical architects to develop and deploy solutions while enhancing your skillset.

Salesforce certified business analyst

The salesforce certified business analyst certification is mainly for people who need to accept their aptitude also skills to understand what is possible with the salesforce step and business values accompanying advantages and failures. The certification focusses mainly on the salesforce workers who need to show their learning & capacity.

Salesforce business analyst course

The area of cloud computing is getting vast due to technological developments. Along, with the developments, it tempts a high number of professionals to deal with the new development of cloud computing. So, there is a large number of people required in this sector. Salesforce business analyst courses play a vital role in the business. Because these people have to take the charge on business issues and also the words exchange in a group of various sections of the business. Their volume is dependent on the organization of the client. The global online course is one of the good courses in this domain. They have the best trainers at a reasonable cost.
These courses provide people to get in-deep knowledge of this platform and increase knowledge about customer relations. Also, it gives hands-on experience in cloud computing. The course includes knowledge about cloud computing, improve analytics and increase the scope for business.

The outcomes of the analyst course

1. Knowledge of field.
2. Experience in doing live projects.
3. After doing the course a person becomes capable of handling customers with more lucidity and fast.
4. The person would be able to help the company to enhance the sales force.
5. In-depth knowledge of cloud computing, page layouts, SOQL, SOSL, etc.

Who should take the salesforce courses?

Salesforce has become more popular because of the high-level benefits and services provided. It is the fact that these professionals have a high salary than other professionals. The person who has done this course can get several jobs and a high salary. The person who can do such courses includes are technical consultants and business analysts. There are many course and certification which are there to become certified in Salesforce business analyst. Different company and organization have made their unique curriculum for the courses and provide certification.

What Are the 6 Applications of Predictive Analytics in Business Intelligence?

Posted on by Imarticus Learning

Understanding Predictive Analytics

The data science discipline has gained huge popularity among corporations given its ability to decode insights from seemingly irrelevant pieces of daily information. Data analytics training is in high demand given the paucity of professionals in the field of data science. Predictive analytics can be understood as a highly advanced version of analytics that is being used to make predictions about unforeseen future events.

The predictive analytics process entails a range of statistical methods like data mining, machine learning, predictive modeling, etc. All these methods are applied to analyze historical and current data to make future predictions. Let’s jump over to some of the most prominent applications of the predictive analytics method in the field of business intelligence.

Audience Targeting 

With the advent of advanced analytics methods like predictive analytics, the marketing game has changed. Audience targeting is all about the personalization of marketing communication with the customer. Here a customer base is segregated into groups based on extensive factors other than the commonly used age, gender, occupation. These factors might include interest, likes, spending habits, transaction history, etc. This helps companies to customize their messaging as per the audience profile and predict those who are more likely to purchase the goods or services.

Risk Analysis

The risk analysis process is a complex one and it plays a huge role in the success of any business venture. It helps to analyze and predict the problems which might occur for a business based on a complex understanding of the variables that affect the business. Predictive analytics is used in this context to help build decision support systems that can help determine the profitability of any business operation. A possible application of this technique is in the banking sector for analyzing the credit risk of borrowers. The variables related to borrowers are factored in to derive conclusions.

Revenue Forecast

Sales forecasting is an important aspect of business intelligence. Any given corporation has to think about the revenue that it’ll generate in the near future. The sales forecast is a complex process involving a lot of variables that influence the sales figures. These variables might include seasonality, market events, macroeconomic factors, general industry trends, etc. Data mining techniques can help assess consumer preference and outlook after factoring in all these variables. The end goal is to predict the demand for a given product or service produced by a firm.

Churn Avoidance 

The cost of acquiring a new customer for any business is far greater than the cost of retaining an existing one. This is why churn prevention is important for enterprises. Churn prevention helps to analyse and predict when and why customers decide to switch to other brands and end their relationship with the company. The companies can maintain a proactive approach to retain their existing customer using predictive analytics by leveraging big customer data sets.

Financial Modelling

The main goal of financial modelling is to create a simplified model of the complex real-world financial landscape that will help to predict and assess the performance of various financial assets. These are mathematical models designed to represent the quantitative performance of financial assets in the near future. In simpler terms, it is all about converting the hypothesis and assumptions regarding the financial markets into numeric figures that represent performance.

Market Analysis

The 21st-century businesses are all about understanding the needs and wants of customers and providing adequate solutions in terms of products and services. It’s far from the traditional business approach of forcing a product or a service using rigorous marketing. Understanding consumer needs requires conducting surveys. Market analysis using surveys helps businesses to understand their customers better, this results in increased profitability and high customer retention.

How Big Data and AI Work Together?

Posted on by Imarticus Learning

Since our childhood, we have been taught to arrange the raw data to get significant information from it, because raw data itself is meaningless as there is no interpretation behind it. Machine learning (specifically AI) helps to gain the required insights from Big Data to make it purposeful.

At very advanced level AI revolves around a brain(artificial) which analyses the data and makes decisions.

The artificial intelligence is much more required in the field of Data analytics because the traditional analytics are failing miserably to operate the large volume of data that is being created. The fact that more data leads to more purposeful insights results in the intervention of AI in Data analytics.

Both AI and Big data go hand in hand. AI helps in playing around with a lot more data and excessive data helps to train the AI in a better way. In other words, as there is no existence of business without customers, the similar way the power of computers is not very useful in the absence of Big data.

Most of us think that AI is increasingly reducing human intervention in decision making, leading to loss of jobs. Actually, the machine can make decisions based only on the facts, whereas humans use their emotional psyche to make decisions.

The integration of AI and Big data is leading to the more purposeful use of the emotional intelligence of humans in coherence with the facts and figures to make better decisions. This coherence along with proper Big Data analytics training helps businesses to identify the interest of customers in minimal time.

Globalization of business: The availability of a wide range of AI tools in the market leads to its adoption by many companies. The integration of Big Data and AI is helping companies to market the same product in different regions of the world by keeping the track of consumer behavior using AI. This helps in smoother functioning of business in culturally diverse regions without harming the sentiments of the consumers.

Change is always consistent
With the changing needs of customers AI should always improve. To bring in the massive changes in AI catering the future needs one must focus on the AI technologies used with Big Data. Some of the AI technologies used in Big data are:

Disorder recognition is a tool helps in fault detection, sensor network, system health with big data technology.

Bayes’ theory is used to determine the probability (Conditional) of occurrence of an event based on the events which have already been occurred. This theory is best used to recognize the pattern of customer’s interaction with the company results in business providing optimized choices, leading to customer satisfaction.

Pattern Recognition is a technique of machine learning in which AI is trained by giving it certain amount of data, followed by correcting its mistakes manually, which makes AI to recognize the pattern. For example, if I want to train my AI to read my handwriting and convert it into typed text, I will first train my AI using my handwritten notes and then correct its mistakes. This way after few attempts, AI would be able to understand my handwritten notes well.

Graph theory: Through the study of graphs and node relationships, the data can be mapped to the linearity. This model is useful and can help big data analysts to recognize patterns.

To summarize with, both Big Data and AI are two most emerging technologies and go hand in hand. One could utilize AI to decide on how to proceed with big data analysis instead of depending on people. Conversely, Big Data could be used by AI in its self-learning and/or decision making.

Business Analyst Certification: Costs and Requirements

Posted on by Imarticus Learning

An emerging key role in businesses of all sizes and industries, business analysts are gaining in both traction and value. The typical business analyst is tasked with creating, overseeing and measuring changes to business processes of operational systems after key insights from data reports. A business analyst is critical to the growth in productivity and efficiency of a company, regardless of its size. Since these players make decisions that can make or break an operation, only the most skilled and highly-certified ones land enviable jobs.

Business analysts are more than capable of interpreting data, paying attention to salient data points and communicating changes to senior management outside the IT department. There are already a plethora of organisations offering certifications to validate your business analysis skills. Business analyst certifications help to prove your fortitude as well as command senior job roles and higher salaries than the rest of the market.

Business analyst certifications are a hallmark of your expertise and experience in the field and negate the need to go into long-drawn explanations of job histories when in an interview. Usually, certifications from the IIBA are valued above all else, because this organisation is the world’s leading association for BA.

If you’re keen on exploring a business analyst certification course, you should know that the fee varies depending on the party offering the course. Some of the more popular certifications are as follows.

Entry Certificate in Business Analysis

This first-level IIBA certification is designed for budding business analysts without much in-field experience. The certification requires that you obtain 21 hours of credits in professional training, and is a great starting point for those looking to move up the ladder.

Cost: US$60 per application and a further US$235 in exam fees for non-members

Certification of Competency in Business Analysis

The second-level IIBA certification has a whole host of requirements, including 3,750 hours of work in the last 7 years that is fully compatible with the BABOK (Business Analysis Book of Knowledge) guide. Since this is a step-up from the previous level, it also needs 21 hours of professional training done within the last four years.

Cost: US$125 per application and US$450 as exam fees for non-members

Certified Business Analysis Professional

This IIBA certification is for extremely experienced business analysts who have managed to complete 7,500 hours of business analysis work over the last 10 years, among other requirements. Hopeful candidates will also need to ensure they have had 35 hours of development experience in the field over the last four years.

Cost: US$125 per application; US$450 as exam fees for non-members

Certified Foundation Level Business Analyst

This certification is offered by the International Qualification Board for Business Analysis (IQBBA) and qualifies successful candidates to appear for higher-level certifications. This foundational level certification covered the gamut of topics from enterprise analysis to design and innovations. Successful candidates can attempt to obtain the Certified Expert Level Business Analyst (CELBA) and Certified Advanced Level Business Analyst (CALBA) certifications after this round.

Cost: US$250 in exam fees

Professional in Business Analysis

A certification bestowed by the Project Management Institute, this one is tailor-made for those business analysts whose job roles involve working with programmes or projects. Alternatively, it is also ideal for programme managers working with analytics. The business analyst certification course revolves around real-world projects and access to tools and software. With an earned bachelor’s degree, hopeful candidates will need 4,500 hours of consecutive BA work experience within the past 8 years. For those who don’t have a bachelor’s degree, this number goes up to 7,500 hours.

Cost: US$555 per application for non-members; no exam fees

A business analyst certification is a great way to prove your worth and validate your experience to secure high-paying jobs across the world!

Also Read: Business Analyst Certification Benefits

A Day In The Life Of A Data Scientist!

Posted on by Imarticus Learning

The data science field holds immense career potential, yet you must be thinking, what actually do data scientists do the entire day?

To provide you deep insights into data scientists’ usual tasks so you can imagine yourself in that role and decide if the time is ripe to get trained for it, we have gathered some insights for you.

No Such Typical Day

If you ask somebody working as a data scientist about their typical working day, he/she may burst into laughter after listening “typical”. For those who are adaptable and flexible, and love to do various responsibilities, then a typical day of data scientists should fit them just fine. While these workdays are subject to changes, some essence of the day stays as it is – working with people, working with data, and working to stay abreast of the field.

Data is Everywhere

Given the job role, it is no surprise that data scientists’ regular tasks hover around data. A major portion of their time is consumed in collecting data, analyzing data, processing data, yet in several ways and for several reasons. Data-centric responsibilities that data scientists may come across include:

  • Pulling, merging and assessing data
  • Searching for trends or patterns
  • Leveraging numerous tools such as Hadoop, R, MATLAB, Hive, PySpark, Python, Excel, and/or SQL
  • Developing predictive models
  • Striving to streamline data issues
  • Developing and testing new algorithms
  • Creating data visualizations
  • Gathering proofs of concepts
  • Noting down outcomes to share with colleagues

Interacting With a Broad Range of Shareholders

This may appear as if it has a minor role in data scientists’ day, yet the otherwise is true as eventually, your job is to ward off issues, not create models.

It is paramount to remember that even though data scientists are playing with data and figures, the reason for this is fueled by a business requirement. Having the ability to view the larger picture from a department’s perspective is vital. So is being able to comprehend the tactic behind the requirement, and to assist people comprehend the consequences of their decisions.

Data scientists dedicate their time in meetings and replying to emails, just like most people do in the corporate sphere. Yet, communication skills may carry greater importance for data scientists. While attending those meetings and responding to those emails, as a data scientist, you should be able to elucidate the science behind the data in layman terms, as well as able to comprehend their issues as they view them, not from data scientists’ viewpoint.

Staying Updated with Changes

Both, working with data as well as with others will account for a notable portion of the day if you decide to pursue a career in the field of data science. The remaining of your day will be captured staying updated with the data science industry. New insights arrive on a daily basis as other data scientists craft a solution to fix an issue, and then extend their new finding.

Data scientists, thus, normally dedicate a portion of the day going through industry-centric articles, newsletters, blogs, and discussion boards. They may attend conferences or connect online with various data scientists. Moreover, occasionally, they may be the ones to extend new insights.

As data scientists, you do not wish to waste time starting from scratch. If anyone else has a better solution to fix an issue, you would like to know. Staying updated with changes is the sole way you will have the ability to do that.

Now the question arises, how to become a data scientist? Well, the good news is you do not have to worry much about it. There are loads of resources available at your doorstep in the form of online courses and e-books. So, if you want to pursue a career as a data scientist, grab these resources and get yourselves enlightened.

The Perks of Using Machine Learning for Small Businesses!

Posted on by Imarticus Learning

Machine Learning and Artificial Intelligence have often been associated with top-of-the-rank brands such as Google and Apple. That has led to the perpetuation of an idea that AI just isn’t for everyone… and that’s incorrect.

Artificial Intelligence, specifically Machine Learning, is just as accessible and usable to small businesses as they are to tech and finance titans. When it comes to staying ahead of competitors, the situation is make-or-break– emerging technology is the portal through which smaller companies can gain headway in an already airtight industry, or quickly adapt processes that take months to approve in larger corporations.

As with anything new, the future of artificial intelligence and Machine Learning also presents its own sets of stumbling blocks, some of which may prove to be a detriment for smaller companies with limited budgets and skilled personnel. R&D accounts for a large chunk of the expenditure; training and analysing models takes topline human resources.

However, if firms are willing to take the risk and take the plunge, there are a whole host of perks that will have small businesses emerging victorious:

Making Marketing Campaigns Stronger

Marketing is the be-all and end-all of many brands, especially those that heavily rely on brand image and word of mouth to sell products or services. Machine learning can be put to use in marketing in the following manners:

  • Personalising product recommendations
  • Automating cataloguing of products
  • Optimising content from email subject lines to Facebook ads
  • Researching trends and search terms
  • Revamping keywords and SEO strategies

To achieve the following goals:

  • Innovative products and services
  • Happy customers and lesser returns
  • Intuitive and interactive user experiences
  • Diversified revenue streams
  • Reduced marketing costs and subsequent waste

Driving Sales Numbers

When it comes to sales, insights and analyses of data can be a veritable goldmine– this is where machine learning comes in. A solid ML tool can analyse:

  • customer-product interactions
  • past purchases
  • digital behaviour
  • trending search terms
  • popular products
  • transaction types

Using this, firms can identify what leads are likely to convert and equally pay attention to converting hesitant users into loyal customers.

Upselling and Cross-selling

Upselling means getting the customer to purchase a higher or more upgraded product, while cross-selling means pitching products in the same segment or complementary to the product in their cart. Machine learning can be leveraged to produce personalised recommendations of products and services based on analyses of the existing database. By identifying past purchases or inter-linking products, machine learning tools can upsell or cross-sell appropriately, thereby driving revenue and increasing the number of items sold.

Automating Repetitive Tasks

Small businesses are often faced with having to delegate the most menial tasks to precious employees, leaving the latter overburdened and unable to innovate. Using machine learning to automate repetitive tasks can ensure that routine measures are taken care of at scheduled times and employees are left with time to think strategically and fulfil intended roles. Some tasks that are automatable include:

  • Generating and sending email responses
  • Setting up a sales pipeline
  • Collecting and logging payments
  • Gathering and evaluating client satisfaction

Conclusion

Regardless of the industry, machine learning offers several perks for small businesses to help them grow, expand and generate revenue through different streams. From bookkeeping and manual data entry to voice assistants and exclusive data insights, a machine learning course can put you at the. Forefront of the industrial revolution taking the world by storm today.

How Statistics Relate to Machine Learning?

Posted on by Imarticus Learning

Introduction

Machine learning and statistics have always been closely related to each other. This led to an argument about whether it was different from machine learning or formed a part of machine learning. Several Machine learning courses specify statistics as one of the perquisites for machine learning.

Hence, we need to develop an understanding of the fact if statistics relate to machine learning and if it does, how?

Individuals working in the field of machine learning concentrate on the task of model building and the result interpretation from the model that was constructed while the statisticians perform the same task but under the cover of a mathematician concentrating more on the mathematical theory involved in the machine learning task concentrating more on the explanation of the predictions made by the machine learning model. So, we can say that in spite of the differences between statistics and machine learning, we need to learn statistics in machine learning.

Statistics and machine learning

Both statistics and machine learning are related to data. Although they work with the data in their way, some requirements are needed by both and hence they form a close relationship with each other. Given below is a step by step analysis as to how statistics relate to machine learning.

Data preprocessing requires statistics

To proceed with the machine learning task, cleaning of data is a mandatory step. This process involves tasks such as identifying missing values, normalization of the values, identifying the outliers, etc. These operations call for statistical concepts such as distributions, mean, median, mode etc.

Model construction and statistics

After the data has been cleaned, the next step is to build a model with that data. A hypothesis test might be needed for model construction which calls for good statistical concepts.

Statistics in evaluation

Model evaluation requires tasks such as validation techniques to be performed so that the accuracy and model performance increases. These validation techniques are easily understood by the statisticians but a bit difficult for the machine learners to interpret as it involves mathematical concepts.

Presenting the model

After the successful construction and evaluation of the model, the model is presented to the general public. The interpretation of results requires a good understanding of concepts such as confidence interval, quantification, an average of the predicted results based on outputs produced and so on.

Other than the above-mentioned steps some additional concepts must be adhered to while working with machine learning. Some of these concepts are listed below:

  • Gaussian distribution – It is often represented by a bell-shaped curve. The bell-shaped curve plays a very important role while normalising the data as a normalised data is supposed to lie at the point where the bell-shaped curve is divided into two equal parts.
  • Correlation– It can be either positive, negative or neutral. A positive correlation indicates that the values change in the same manner(positive causes positive and negative leads to negative). A negative correlation indicates values change oppositely while neural suggests no relationship. This concept is of great importance to the analysts while identifying the tendencies in the data.
  • Hypothesis- An assumption might be done for the elementary predictive analysis in machine learning that requires a good understanding of the hypothesis.
  • Probability – Probability plays an important role in predicting the possible class values in classification tasks and hence forms an important part in machine learning.

Conclusion

Statistics is of huge importance to machine learning, especially in the analysis field. It is one of the key concepts for data visualization and pattern recognition. It is widely used in regression and classification and helps in establishing a relationship between data points. Hence, statistics and machine learning go hand in hand.

What Is Big Data Analytics Training Online?

Posted on by Imarticus Learning

What is Big Data?

Big data is the analysis of large datasets to find trends, links or other invisible visions with small data sets or traditional operations. The burgeoning growth of devices and sensors connected to the Internet is a major factor requiring hundreds or thousands of computers for big data, storage, processing, and analysis.

An example of big data used in the development of autonomous vehicles- Self-driving car sensors can detect and analyze millions of data points to improve performance and avoid accidents.

Big Data Online Course

Learn the basics of big data and learn how to design and implement a Big Data Analysis solution using this free online course that presents this required field. Master technologies like Hadoop, Azure, and Spark and their implementation.

For big data pre-approval, consider the Microsoft Professional course with 15 big data courses. This multi-unit program is designed to pave the way for a new career. Learn how to handle real-time data flow and implement big, real-time data analysis solutions.

You will learn analytics and artificial intelligence and the use of Spark for implementing analytical solutions. This is one of the major benefits of big data. Start with a self-guided course that covers the basics of big data technologies, data formats, and databases.

About this course

Acquiring basic skills in today’s digital age and storing, processing, and analyzing data help you make business decisions.

As part of the Big Data program, this course will deepen your Big Data Analytics knowledge and improve your programming, Analytics and Artificial Intelligence Training. You will learn how to use basic tools like Apache Spark and R.

The topics for this course are:

  • A big analysis of cloud-based data
  • The predictive analysis includes stochastic and statistical models.
  • Extensive application for data analysis
  • Analysis of problem space and data needs.
  • At the end of this course, you will address a wide range of data science issues with creativity and initiatives.

Big data function

If you like data processing, analytics, and computer programming and want to join one of the hottest areas, big data is the best choice. Big companies like Amazon AWS, Microsoft, IBM, and LinkedIn are trying to broaden their horizons. At the time of this article, Big Data had already included more than 1,600 full-time jobs with an estimated salary ranging from $ 90,000 to $ 140,000 per year. Senior positions include big data developers, big data engineers, and big architects.

Workers are responsible for building big data analytics systems of big data in real-time. Because the Internet of Things (IoT) generates large amounts of data, companies need to find a way to get more ideas to stay competitive.

The demand for professionals who can design big data solutions is high and salaries are very competitive. Required programming languages ​​and tools include C ++, Hadoop, Sparks, HDFS, Soop, Scala, MapReduce, Spark, Java, Apache Hadoop, Apache, Python, SQL, and more.

Explore big data careers with online courses

Learn the basics of big data platforms like Hive, HBase, and Kafka, SQL, Hadoop, Pig, Spark and see if your exciting career is right for you. Start with a Microsoft introductory course and proceed to a full certification program. The basic course is self-contained, so you can sign up and start studying today!