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How To Become Data Analyst After 12th?

Posted on August 12, 2021January 20, 2024 by Imarticus Learning

Data science, especially data analytics, is becoming the most desired job in the world with time. With multiple job positions and abundant opportunities, it is only wise to opt for data science courses after 12th or graduation. Students aspiring to be data analysts need to have a clear picture of how to become a data analyst and what data scientists do.

The first thing that an aspirant needs to know is that data analytics is a subset of data science, and hence knowing what is data science is paramount to learning what data analytics is.

Why a career in data analytics?

As data is becoming the most essential commodity to organizations worldwide, it is only wise to opt for a data science course in India. There are many benefits of data science courses and the most important one is that you can start a career in data analytics.

Business analytics courses will also boost the knowledge of a candidate in data analytics as it will help in understanding the domain better. Before understanding the requirements let us list the benefits of a job in analytics:

There are many job opportunities in the domain. Thousands of vacant positions are only waiting to be filled by qualified candidates
The salary of a data analyst is even higher than that of an IT professional
There are different domains within data analytics that can be a great option for career and salary growth
A challenging and stimulating work environment with a great work-life balance
An elite lifestyle

What are the requirements to become a data analyst?

You can opt for data science courses after 12th for UG. But, there are further options to start a data science course in India even after UG at PG or doctorate levels. You can also opt for certifications and diploma courses after the 12th.

At the postgraduate level, you can opt for a specialization in computer management and computer science to start your analytics career.

For pursuing a postgraduate program, a bachelor’s degree with a minimum of 50% marks is required, preferably in computer science or data science and from a recognized university.

Apart from educational qualifications, several soft skills like analytical and numerical skills are important for pursuing a career in data analytics. Further, a deep and thorough understanding of computer software and programming languages including querying languages (like Hive, SQL, and Pig), scripting languages (including Python and Matlab), statistical languages (such as R, SPSS, and SAS), and Excel is a must.

Besides, data analysts must possess problem-solving and interpretive skills to explain and present the process of data analysis and its results to decision-makers.

A data analytics course can help a student after 12th to bag high-paying jobs like data scientist, data engineer, database administrator, data analyst, and data architect. If you want to expand your job horizon, then you can even choose to do business analytics courses to end up in even higher positions with better commissions.

Take Advantage of This Once-In-A-Lifetime Opportunity To Express Your Ideas And Win Fantastic Prizes.

Posted on August 11, 2021May 30, 2025 by Imarticus Learning

Are you a Data science blogger? Imarticus Data Science is proud to announce our Data Science Blogging contest. This contest will reward the best Data Science blog posts of 2021 with fantastic prizes with up to 10,000 gifts vouchers.

Do you feel like you have many insightful thoughts to share as a blog on Data Science & Analytics? If you enjoy writing about data science, there is a once-in-a-lifetime opportunity to put your ideas in front of countrywide audiences. And the best blog post author stands to win a prize for their work!

Share your blog on any of the following topics:

Data science
Data Analytics
Machine Learning
Data Engineering
Deep Learning
Computer Vision
Python Programming and many more related to Data Analytics topics.

The Criteria to Participate in Data science Blogging contest

All blogs should be 500 to 1000 words in length.
The content must be original, well-researched, plagiarism-free, and informative.
Do not entertain duplicate posts.
The deadline is August 31st, 2021, at 11:59 pm IST.
The number of article contributors is restricted to three members.
blog.imarticus.org will host all the articles with credit given to the contributor(s). Blog entries are considered Imarticus Learning intellectual property from this point onward.

How to enter in the Data science Blogging contest and the process

Write a blog on a topic of your choice pertaining the data science and analytics. After completion, share your blog at blog@imarticus.com on or before August 31st, 2021, 11:59 pm, Indian Standard Time (IST).
The originality, creativity, and level of depth in all blog articles.
The content should meet the minimum criteria explained above and be submitted before the given deadline.
Will upload all the eligible blogs on or before September 11th, 2021.
The writers will receive the respective blog links by September 11th, 2021. The individual should share the blog link on their social channels with mandatory hashtag rules.
Imarticus team will evaluate the engagement on the individual blog posts until September 30th, 2021. The Imarticus panel team will shortlist the best 25 blogs and promotes them on their social channels until October 30th, 2021.
The blog that receives the most engagement by October 30th, 2021, is shortlisted as the winner. Imarticus Editorial Panel’s decision is final and binding in case of any dispute.

Why should you participate in the Imarticus Blogger of Year Contest?

Imarticus Social recognizes your skill and is eager to help promote your blog.
Exiting winning amount to motivate and encourage your effort.
The winner details will get necessary coverage within the media promoted and supported by Imarticus Learning.
We will promote the interview of the top 25 selected bloggers on different social channels of Imarticus Learning.

Apart from the 10,000 gift voucher to the winner, Imarticus Learning will give prizes to the other participants. The details are as follows:

Winner: 10,000
Runner Up: 7,500
3rd Place: 5,000
4th to 10th Position: 2,000
11th to 20th Position: Imarticus Hall of Fame Entry

T&C Apply.
Imarticus Learning shall own the Intellectual Rights of the blog content shared with us at blog@imarticus.com with due credits to the writer(s) till perpetuity. Imarticus Learning reserves all the rights to use, publish or remove the content on all our platforms.

The decision of the Imarticus Editorial Panel shall be final and binding in all matters. Any dispute will fall under the jurisdiction of Mumbai. The winners will receive Gift Vouchers.
To know more – Click here

Conclusion:

If you are interested in data science and want to share your ideas with the world, then this is a once-in-a-lifetime opportunity. Entering our #ImarticusBlogLikeAPro Season 1 Championship Award along with a cash prize of INR 10,000/ will not only is fun, but it could also win you fantastic prizes! Professional tone required for submission.

Customer Data Mapping, Engagement and Developing Trust with Data Analytics!

Posted on August 11, 2021March 29, 2024 by Imarticus Learning

Data analytics is the new talk of the town. You might be planning to learn something online and wondering if you should do a data analytics course or a certification in data analytics, then this article will tell you the reasons to learn data analytics online and how in every business sector data analytics is getting more relevant every day.

To ensure the success of any business, developing trust and ensuring customer satisfaction has always been a key recipe. The introduction of analytics in customer data mapping has completely transformed the way businesses engage with their customers and win their trust.

With proper customer data utilization using analytics, businesses are able to engage customers in a more personalized way. Many organizations are reaping the benefit of using analytics to improve customer engagement. Analytics allow using intelligence in the customer data to provide tailor-made offerings. Several factors like using various data sources, well-developed core analytics capabilities and integration of AI and IoT into processes make this possible.

Key trends in customer engagement using analytics:

Growth is likely to continue:

More companies have started using analytics for better customer satisfaction, and this percentage is growing each year.

Analytics going to be the main driving force:

This has been observed that organizations that are more experienced in using analytics than their competitors are able to gain more trust and provide more customer satisfaction.

Analytically experienced are using more data:

Analytically experienced organizations tend to use more data from all possible sources when compared to lesser experienced organizations.

Data sources, like customer, vendor, regulator, and competitor data, and data types, like mobile, social, and public data, all are getting used and playing a major role.

Key points for better customer mapping

Data source and data types:

Large in volume and variation ensures quality data. When different types of data like mobile, social, and public data are collected from various sources like customers, vendors, regulators, and competitors, analytics can lead you to a more accurate forecast.

Integrated system:

By using the data-based dashboard while fixing your customer strategy, the scope of guesswork comes down to null. Data analytics systems integrate into existing infrastructure with minimal effort and without a need for overall change. Integrating new data and analytics into the existing model improves your customer service.

Innovation to turn customer mapping into customer satisfaction

Data mapping using analytics takes traditional data mapping to a whole new level. This works as the best foundation for decision-making. These strategic changes could include social media strategy, website upgrades, and many other things.

Building profiles using Analytics

Analytics helps to identify each client independently, based on their intercommunications throughout their journey with the business. Businesses can then trace and gather precious data for future use. Analytics can build individual customer profiles using this data based on real-time action, habits, and inclinations.

Importance of Qualitative data

Few analytical tools support solutions that take qualitative data into account. Knowing how happy customers are, key phrases they use, or survey feedback are all forms of qualitative data. Quantitative data analytics and qualitative customer experiences must be equally prioritized to ensure a better result.

Prioritization of personalization

Incorporating customer journey analytics into strategy is important. Using analytics, the appeased customer is going to receive can be personalized and segmented. When customers receive more personalized and relevant content, they are likely to be more interested.

Conclusion

If you want to learn data analytics online, then Imarticus offers you a data analytics course and certification in a data analytics program that you might be interested in.

Mapping customer data, understanding the buyer’s persona (a fictional identity of a buyer based on customer data), and going the extra mile to meet the customer’s demands can really help businesses, and data analytics is the way to go.

Business Simulations – The What, How & Why?

Posted on August 11, 2021October 12, 2022 by Imarticus Learning

What are business simulations?

Business simulations are highly interactive learning tools that provide a hands-on experience to the participants. They focus on practical methodologies like building skills while learning, improving knowledge on concepts, and allow to grow by looking at the bigger picture.

Such experiential learning ways to train participants in various fields can help engage them among themselves while doing their best for their own growth and development and at the same time, help the organization achieve its goals.

Business simulations can be chosen based on four major factors:

Technology

Simulations based on technology can be beneficial when avoiding paper-based simulations. They carry an ability to visualize the learning and can gather participant data. They simplify complex data to make learning an easier experience with more interactivity and personalization.

Response and Feedback

Simulations can create a realistic context but they can also respond to various inputs by the learners immediately or provide feedback once done. This is the input-response-feedback cycle. The inputs can be a financial decision or a dialogue or questions.

Responses can be in the form of visual prompts, financial calculation indicators, etc., such as a budget change. Feedback comes in the end when the participants see how their inputs affect the responses. For example, through a heat map or financial report, etc.

Realism

Unlike case studies or role-plays, simulations can replicate an external situation for participants to make decisions in a virtual environment which is realistic, so that they can make similar decisions in real environment on-the-job. A limitation to realism is that if taken to extremes, it may complicate the learning environment which may lead to a distracting or inappropriate application.

Process and Outcome-Focussed

Simulation-based learning involves a learning process which could be in the form of dialogue or competition, driven by the learners, and the final result. The outcomes can be intrapersonal, interpersonal, external, and related to business. The intrapersonal outcome implies whether the participants have learned anything about themselves.

Interpersonal is based on the question of whether the participants were able to cooperate, coordinate and develop relationships. Business based outcome checks whether the learners were able to create value. External outcomes involve information regarding how the learners’ decisions fit into the context of the organization’s values and the community and within the industry.

The most interesting fact about business simulations is that they require participants to implement what they learn in a risk-free environment. Thus, encouraging them to appreciate the business strategy and business management systems to improve skills, performance, and growth. In short, business simulations function as a bridge to fill the gap between theoretical learning and practical or real-life learning experience. For instance, the participants can make relevant decisions in a challenging environment or situation, similar to what their role demands at their organization.

Business simulations allow the learners to study the markets, its participants and act accordingly, based on their observations, strategically or operationally.

How do business simulations work?

Josh Bersin said that “Learners retain only 5% of what they listen and 10% of what they read, but they remember more than 50% of what they learn through discussion and interaction”.

They create learners who engage more

Interactive learning methods are useful to create more discussions, help in making learning fun while retaining more information and develop relevant skills.

Easy retention through immediate application

By immediately implementing the learned concepts, the participants learn by doing, thus, retaining more information quickly. This stickiness is beneficial for the transfer of knowledge in real-life situations at work.

Simulation helps abstract reality

Simulation games can help recreate real-world experiences that can be practiced by participants in a risk-free environment.

Learners get more empowered

Participants can understand how the businesses work in a much detailed manner, by taking control and making choices differently.

Business Simulations prepare participants for real-world problems

By the time learners encounter similar situations in their business environment, they are equipped with skills and techniques to be well-prepared for real-life issues.

Business simulations can be blended

Through live classrooms, eLearning sessions, virtual classrooms or blended deliveries, business simulations can be delivered to the learners. The delivery experience can be chosen based on the choice of the participants at various levels.

They build networks

Business simulations promote the formation of vast communities or networks of learners who can interact with each other.

Why use business simulations?

Business simulations come with several benefits, but here are the key reasons why they should be used for teaching concepts:

Business simulations can imitate the on-the-job learning style

According to many researchers in the corporate training world, more than 70% of what participants learn comes from experience, 20% is learned through interactive learning, and 10% is due to traditional learning methodologies, which may include reading and case studies. Business simulations can imitate real-life situations and be useful in replicating on-the-job.

They provide risk-free decision making

Business simulations allow participants to experience learning in a realistic but risk-free environment. This means that the learners get opportunities to make decisions and mistakes which do not cost the organizations. Without any fateful consequences, the participants can change their behaviors and attitudes according to the situation by learning from experience.

Business simulations are realistic

By replicating realistic market environments, business simulations can be an effective tool for learning by using real and complex situations and implementing the same in corporate environments. The success of simulation is when there is no difference between the game simulation and the real business.

They help participants use their time effectively

Simulations come with limited time, which creates an environment for quick decision-making. In this way, the learners are forced into making decisions under pressure but also ensure that the right decision is made within the deadline.

Simulations create a common culture among the learners

There is no better tool than business simulations to create a common goal for a group of learners. Through teamwork, the participants can think and act together to make decisions and resolve conflicts.

What is The Difference Between Data Analysis and Data Science?

Posted on August 10, 2021November 29, 2023 by Imarticus Learning

Following the current technological transformations within the economy, there has been an emergence of enormous career options, wherein, Data Science is the hottest. According to the Glassdoor, Data Science arose as the highest-paid area. On the other hand, there is a significant field that has been gazing attention for years, i.e., Data Analysis. Both the Data Science and Data Analysis is often confused by the individuals.

However, the terms are incredibly different in accordance with their job roles and the contribution they do to the businesses. But, are these the only factors that make these two distinct from each other? Well, to know more we need to take a look below:

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Also Read: Top 5 Data Science Trends in 2018

Data Analysis Data Science:

Data Analysis is referred to as the process of accumulating the data and then analyzing it to persuade the decision making for the business. The analysis is undertaken with a business goal and impact the strategies. Whereas, Data Science is a much broader concept where a set of tools and techniques are implied to extract the insights from the data. It involves several aspects of mathematics, statistics, scientific methods, etc. to drive the essential analysis of data

Skills:

The individuals misinterpret Data Analysis with Data Science, but the methodologies for both are diverse. The skillset for the two are distinct as well. The fundamental skills required for Data Analysis are Data Visualisation, HIVE, and PIG, Communication Skills, Mathematics, In-Depth understanding of R and Python and Statistics. On the other hand, the Data Science embed the skills like – Machine Learning, Analytical Skills, Database Coding, SAS/R, understanding of Bayesian Networks and Hive

Techniques:

Though the areas – Data Analysis and Data Science, are often confused about being similar, but the methodology is different for both. The methods used in the two are diverse. The essential techniques used in Data Analysis are – Data Mining, Regression, Network Analysis, Simulation, Time Series Analysis, Genetic Algorithms and so on. While, the Data Science involves – Split Testing, categorizing the issues, cluster analysis and so on

Aim:

Just like the areas are different, so are their goals. The Data analysis is basically about answering the questions generated, for the betterment of the businesses. While Data Science is concerned with shaping the questions followed by answering The Data science, as illustrated above, is a more profound concept

The era of Artificial Intelligence and Machine Learning is shaping the economy in a much more comprehensive aspect. The organizations are moving towards a data-driven decision-making process. The data is becoming imperative in functioning and is not limited to the Information Technology organizations.

It is soon taking over the industries like – Sports, Medicine, Hospitality, etc. Such technological advancements have led to a rise in job opportunities in the area of Data Science and Analysis. The merely significant facet which needs to be taken into consideration is the understanding of the difference between the two. Big Data is the future which is expected to lay a considerable impact on the operations of both industries and routine life.

Related Article: What a Data Scientist Could Do?

How The Machine Learning Works Behind Your Favorite Google Meet Backgrounds?

Posted on August 9, 2021March 22, 2024 by Imarticus Learning

Google Meet has been a lifesaver for many professionals and students who are unable to step out of their homes for the last few months. This increasing usage of such virtual meeting platforms has improved the technology reach. This and Google AI has now increased the need for Machine Learning training and opened up a whole new world in technology.

Google’s AI of Google Meet now allows the user to change the background and reduce the noise level as well. Instead of the boring or the interiors of the home as the background, Machine Learning has helped customize the backgrounds for such meetings.

The technology behind the backgrounds

Google uses MediaPipe Objectron to get the 3D dimensions of images on mobile devices. It is also useful for background changes as well. They came up with an in-browser version of the Machine Learning model that can blur or replace the backgrounds. With these combined efforts of the ML, MediaPipe, and the OpenGL technology, its performance is better even in the devices with low power available.

Google uses WebGL for rendering, ML such as TFLite, and ZNNPack for web-based interference.

How does it work?

The MediaPipe uses the new low-level format of the binary code of WedAssembly. This can speed up the processing faster than JavaScript and can improve the speed of the tasks as well. The instructions from the WedAssembly are converted into simpler code by the browser.

First of all the ML segregates the user and its background.
Now, the user is masked by the ML interference into a low-resolution component.
The mask undergoes processing to refine its edges to be a smooth blend with the new background.
A WebGL2 is used to get the final output for the video where the mask is suitable with the replaced or the blurred background.

The technology here uses a lighter interference that uses less power and smaller storage space.

Refining the results

Although the masking effect is refined so it makes it easier to blend with the background, it could still end up having a halo effect. The light wrapping disables this possibility. The composting technique refines the edges of the mask and also allows the background light to adjust itself to blend the user with itself. The technique allows the light from the background to spill all over the edges of the mask to conceal the halo effect. This results in the fine blending of the background with the foreground image.

Performance in various devices

In the high-end devices, the image transition through the ML system continues at a higher resolution but in the low-end devices, there is a slight change. In the latter, the working mechanism automatically switches through the lighter models of ML so as to maintain the performance speed. Here, it skips the image refining process to send the final output.

The flexible configuration of the MediaPipe enables it to choose the most effective processing method.

Google AI and ML

The regular updates on Google AI and algorithms have opened new scope in the field of Machine Learning and its various prospects. While the Machine Learning Course provides basic knowledge, there is more to it when it is learned properly.

Since the internet-based virtual meetings are not going to disappear anytime soon, more changes in the working are expected. With each change, there is more to learn which naturally increases the importance of Machine learning and AI.

Bottom Line

Seeing all these, it would be not a bad idea to enroll in a machine learning course to start with the basics. Though this is a field with no limits, there is sure a lot to learn.

The Growing Need of Data Storytelling as Salient Analytical Skill!

Posted on August 9, 2021March 22, 2024 by Imarticus Learning

Data storytelling is a methodology used to convey information to a specific audience with a narrative. It makes the data insights understandable to fellow workers by using natural language statements & storytelling. Three key elements which are data, visuals, and narrative are combined & used for data storytelling.

The data analysis results are converted into layman’s language via data storytelling so that the non-analytical people can also understand it. Data storytelling in a firm keeps the employees more informed and better business decisions can be made. Let us see more about how data storytelling is an important analytical skill & how it will help in building a successful Big Data Career.

Benefits of Data Storytelling

The benefits of data storytelling are as follows:

Stories have always been an important part of human civilization. One can understand the context better via a story. Complex data sets can be visualized and then data insights can be shared simply through a story to non-analytical people too.
Data storytelling helps in making informed decisions & stakeholders can understand the insights via Data storytelling and you can compel them to make a decision.
Data analytics is about numbers and insights but with data storytelling, you make your data analytics results more interesting.
The risks associated with any particular process can be explained to the stakeholders, employees in simple terms.
According to reports, more data is produced from 2013 than produced in all human history. To manage this big data and to make data insights accessible to all, data storytelling is a must.

Tips for Making a Better Data Story

If you are running an organization, make sure to involve stakeholders/investors in data storytelling. This helps in increasing clarity in communication and they do not find a lack of information.
Make sure to embed numerical values with interesting plots for a data story. Our brains are designed to conceive visual information faster. Only numerical insights will make the data story boring and more complex to understand. The data insights should be conveyed in a layman’s language through a data story.
Data visualization should be used for data storytelling but it should not hide the critical highlights in the data set.
Make sure you imbibe all the three aspects of data storytelling which are visuals, data & narrative. The excess of any attribute can hamper the effectiveness of your data story.
The outliers/exception in the data set should be analyzed and included in your data story.

The Growing Need for Data Storytelling

New ways of data analytics like augmented analysis, data storytelling, etc. are surging a lot in recent days due to the high rate of data production by firms/businesses. One can learn analytical skills from a Data Analytics course from Imarticus Learning. To build a successful Big Data Career, you will need to learn these new concepts in data analytics.

Conclusion

Imarticus Learning is one of the leading online course providers in the country. You can learn key skills via a Data Analytics course from industry experts provided by Imarticus Learning. Start learning data storytelling now!

Top 5 data scientists salaries by location in India!

Posted on August 9, 2021November 29, 2023 by Imarticus Learning

“Data is the new oil” – this quote by Mukesh Ambani perfectly captures the radical shift our world is going through in terms of data collection, processing, and utilization. Thus, in the current context, the job of a data scientist has become dearer to a major chunk of individuals who are actively looking to make radical strides in data science career opportunities.

This is even more prominent in a country like India where a big proportion of the population belongs to the tech-savvy young generation who are actively looking for Artificial Intelligence Trainings to earn decent salaries in the future.

Let us have a look at the top 5 locations in India in terms of salaries paid to data scientists and examine the reasons for the same:

Bangalore – The capital of the state of Karnataka, as well as the start-up and IT capital of the country, has a huge base of young IT professionals working in some of the biggest IT companies of the world.
This city has been nicknamed the Silicon Valley of India, after the Silicon Valley of the USA which is the biggest tech hub of the world. The average salary of a data scientist is highest in Banglore; around INR 10 lakhs p.a. and it also ranks number one in terms of data science job scope in the entire country.
Chennai – After Bangalore, Chennai is the growing hub of outsourcing data jobs in the country. It has huge IT parks which provide immense opportunities to the emerging techies of the country. The average salary of a data scientist here is a little over INR 9.5 lakhs p.a.
Mumbai – It is the financial capital of the country. Given the scope of IT and data analytics in finances, it is predicted that Mumbai may soon become the largest tech hub of India. Moreover, due to the sheer number of prominent business houses headquartered in this region, Mumbai has the potential to comprise the highest paying jobs for data scientists. Currently, the average salary is around INR 9.1 lakhs p.a.
Hyderabad – Hyderabad is the capital city of the state of Andhra Pradesh and is the emerging hub of tech startups in the country. Big names in the global IT sector industries like Amazon and Google have invested heavily to build the tech infrastructure of this city. Hence, it is quickly emerging as an emerging market of data scientist jobs with an average salary of INR 8.5 lakh p.a.
New Delhi – The capital of India is the fifth-largest city in terms of the average salary paid to data scientists. It has the highest concentration of data scientist talents which has made it a major hub for tech companies in India. The average salary of data scientists at New Delhi is around INR 8.3 lakhs p.a.

In conclusion, it can be said that India has one of the fastest-growing job markets for data scientists in the world. The recent boom of talented young professionals and the growth of BPO and KPOs have contributed to the development of this sector.

Tutorial for Data Prep – A Python Library to Prepare the Data Before The Training!

Posted on August 8, 2021May 14, 2024 by Imarticus Learning

To get accurate and correct results of a machine learning model, you must prepare your data before its usage. Various applications like the DataPrep can prove to help complete such a tiresome work quickly and efficiently. Without making many efforts, with just a couple of lines of coding, the data can be prepared.

Applications like DataPrep assist the user to explore the attributes and the properties of the data in use. In the recent modifications of the application, advanced aspects like the EDA, short for Exploratory Data Analysis can be found which has been working like never before.

How to use DataPrep?

To make the best use of DataPrep, follow these simple tips.

Import required libraries

The first and the foremost step to begin with DataPrep is to install necessary libraries. Generally, different features in DataPrep can be used through different functions and these functions need to be installed before getting started with preparing the data. Initially, a plot function needs to be downloaded which can be effectively used to visualize the properties and other statistical plots of the data under consideration. After this, you will have to import Plotly Express which is further required to download the datasets which you will be working on.

Importing datasets

For importing the datasets, click on the option of import data sets by being on the flow page. For comparison or better presentation of the data, importing is paramount. You can import more than one data at the same time. This can be done by selecting ‘choose a file or folder’ and click the ‘pencil icon’ and insert the desired file. The files inserted can be renamed for a better understanding.

Exploratory data analysis

To begin with, you need to do statistical data exploration and detailed analysis. You can make use of the plot function for this part of statistical data exploration. Generally, the whole data can be converted into a detailed analysis by just using a single line of coding.

After filling in the code you will be able to see the statistical properties, their frequency and their count. In case you wish to get a display of the dataset statistics, you may select the option of ‘Show Stats Info’ on the screen itself.

If you want to explore the data through its individual and separate attributes and not the whole together, it is possible and quite convenient. Exploring individual attributes of the data provides a clear idea about every aspect. Moreover, it supports various plots like the Box Plot etc.

Plot correlation

In the next step, the plot needs to be imported and correlated so that a heat map for different attributes of statistical data can be created out of it. Heatmaps provide a lucid relationship between all the different attributes of the statistical data. DataPrep provides you with three variants of heatmaps.

Finding the missing Data

Lastly, any missing data in the datasets must be searched so that a replacement can be made in case the data found is not required. For finding the data, use of advertising datasets can be made which can highlight at least some of the missing data.

Conclusion

DataPrep works efficiently with python. However, python is not an easy coding language to lay your hands on without having proper Python training.

You may consider Imarticus learning for getting professional assistance for the different subject matter. A python programming course can also be taken up at Imarticus for a deep insight into python.

How to Start a Career in Machine Learning?

Posted on August 7, 2021December 15, 2022 by Imarticus Learning

The field of Machine learning is expanding fast nowadays with the application of smart algorithms being applied from apps to emails to as far as marketing campaigns. What this means is that machine learning or Artificial Intelligence is the new in-demand career option you can choose.
But being a new field comparatively, you may have many doubts and confusion as to how you can actually get yourself to adopt Machine learning as a career. Let’s ponder over some things you need to master to get your career in machine learning startup.

Understand the Field First

It is an obvious but important fact. Understanding the concept of machine learning and basic math behind it along with the alternative technology while also having hands-on experience with the technology is the key to dive into this field at first.

Covert Problems in Mathematics

Having a logical mind is imperative in machine learning. You need to be able to blend technology, analysis and math together in this field. Your focus on technology must be strong and you must possess curiosity along with openness toward business problems. The ability to pronounce a business problem into a mathematical one will take you long into the field only.

Background in Data Analysis

A background in data analysis is perfect for transitioning or getting into machine learning as a career. An analytical mindset is crucial for success in the field, which means one has to possess the ability to ponder over causes, consequences and discipline to search for the data and digging into it, understand the working and its consequences.

Gain Knowledge of The Industry First

Machine learning, like any other industry, possesses its own unique needs and goals. Therefore, the more you research and learn about your desired industry, the better you’ll do here. You have to study the basic and everyday working of the industry along with all the technicalities involved in it.

Where to Find Work as Machine Learning Expert

Job portals are a good way to find work in your starting days in machine learning. You can apply for a job in portals such as Indeed.com, Monster, Glassdoor, etc. You can sign up on some freelancing site (such as Upwork) too to get your starting assignment as a machine learning expert.

The Best Companies to Work For in The Field

Two types of companies can provide you machine learning job as present: huge MNCs and established companies, or start-up businesses. There are two basic markets at present for machine learning experts for you to tap on. First is the Cloud and the other is the logs, which allows companies or analytics to let customers create their own algorithms.
The large companies which dominate the data analysis and Machine learning field include Databricks and IBM Watson Analytics. Google has also made forays into the AI recently while many of its partners are also looking for professionals to get their machine learning initiative started.

Related Post : What are The Skills You Need to Become a Machine Learning Engineer?