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Category: Data Science training

Get Ready for a Rewarding Career in Data Science

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Get Ready for a Rewarding Career in Data Science

With the big data field experiencing an exponential growth, the need for skilled professionals to sort, analyze data is also growing. Not just businesses but other sectors too are realizing the significance of big data to leverage their growth.

In order to move forward with confidence, big data can help. With digitization the amount of data being generated is also increasing and to process such vast amount of data skilled professionals are required.

The field is surely opening up for the young generation who needs the right blend of skill and passion to land high-paying jobs in the field. Help is available in the form of training institutes which offer cutting edge courses like big data training in gurgaon.

So how much data we are talking about here?

The amount of data that is generated now thanks to IOT, stands at more than 2.5 quintillion bytes of data and this amount is being generated everyday as per the sixth edition of DOMO’s report. By this current year it was estimated that every person will create 1.7MB of data every second.

With IOT being primarily the reason behind this data proliferation, we are looking at a huge data avalanche heading our way comprising mostly unstructured data.

All of the data generated along with past stock are of importance now as crucial sectors like banking, healthcare, communication, manufacturing, finance are being reliant on data to extract valuable information for taking pivotal decisions.

 A Data analyst training institute can be of immense value as they take up the responsibility of shaping data skills of the professionals needed by these sectors.

The expanding field of data requires data experts

Processing through mountains of unstructured data, cleaning it, preparing it for further processing and then analyzing it to find pattern takes skill which could be attained by pursuing Data science using python training.

As per survey findings, there is a huge gap in the demand and supply chain. The field might be expanding and organizations being eager to embrace the power of data, but, the dearth of professionals is posing a big problem which is why the companies in dire need of trained workforce are taking the salary graph higher to lure talent.

However, there are courses available such as business analyst training delhi, that are aimed at training up the new generation of geeks to handle the big data, thereby helping them carve out successful career avenues.

What are the trending jobs in this sector?

Data scientist

A data scientist basically works with a business organization to process raw data, cleaning, analyzing the data to detect patterns that could be of immense value for the organization concerned. A data scientist can play a big role in helping a company decide the next business strategy. They also create algorithms and build machine learning models.  Data Science training can help you be prepared for such a high-profile position.

In the USA, a data scientist can earn upto $1,13,309, while in India it could be ₹500,000 per annum.

Data Engineer

A data engineer is a person who is well versed in programming and SQL, and works with stored data. He basically has to work with data systems and is charged with the responsibility of creating data infrastructure and maintaining it. A data engineer also works to build data pipelines to channelize valuable data to data analysts and scientists fast.

The salary range of a data engineer in the USA could be near $128,722 per annum and in India it could hover around ₹839,565.

Data Analyst

The data analyst is basically the guy who runs the show as he is in charge of manipulating huge data sets. He is involved with the tasks of gathering data and he also creates databases, analytics models,  extracts information and analyzes that to aid in decision making. Not just that but he also needs to present the insight into a format that everybody can grasp.

Having a background in computer science, statistics could give you a great boost along with pursuing business analysis training in delhi.

If you aim to grab this job then you could expect a pay around $62,453 in United States. In India that number might be around ₹419135 on average.

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BI Analyst

A BI Analyst has to put his entire focus on analyzing data in order to identify the potential areas for a company to prosper along with the main obstacles standing in their way to success. They have to update the database on a continuous basis along with monitoring the performance of rivals in the field concerned.

Along with possessing sharp business acumen, he must be proficient in data handling. He basically offers data-driven insight while donning the role of a consultant.

A background in computer science or, business administration, statistics, finance could work in your favor if only you can couple that with big data courses in delhi.

A skilled BI Analyst could expect a pay around $94906 in the USA, and in India they might get upto ₹577745.

There are more lucrative job opportunities and exciting job roles awaiting the next generation of professionals that can help them build a highly successful career. Regardless of which background they hail from undergoing a Data Science course can push them in the right direction.

 


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A Quick Guide to Data Mining

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A Quick Guide to Data Mining

Data mining refers to processing mountainous amount of data that pile up, to detect patterns and offer useful insight to businesses to strategize better. The data in question could be both structured and unstructured datasets containing valuable information and which if and when processed using the right technique could lead towards solutions.

Enrolling in a Data analyst training institute, can help the professionals involved in this field hone their skills. Now that we have learned what data mining is, let’s have a look at the data mining techniques employed for refining data.  

Data cleaning

Since the data we are talking about is mostly unstructured data it could be erroneous, corrupt data. So, before the data processing can even begin it is essential to rectify or, eliminate such data from the data sets and thus preparing the ground for the next phases of operations. Data cleaning enhances data quality and ensures faster processing of data to generate insight. Data Science training is essential to be familiar with the process of data mining.

Classification analysis

Classification analysis is a complicated data mining technique which basically is about data segmentation. To be more precise it is decided which category an observation might belong to. While working with various data different attributes of the data are analyzed and the class or, segments they belong to are identified, then using algorithms further information is extracted.   

Regression analysis

Regression analysis basically refers to the method of deciding the correlation between variables. Using this method how one variable influences the other could be decided. It basically allows the data analyst to decide which variable is of importance and which could be left out. Regression analysis basically helps to predict.  

Anomaly detection

Anomaly detection is the technique that detects data points, observations in a dataset, that deviate from an expected or, normal pattern or behavior. This anomaly could point to some fault or, could lead towards the discovery of an exception that might offer new potential. In fields like health monitoring, or security this could be invaluable.

Clustering

This data mining technique is somewhat similar to classification analysis, but, different in the way that here data objects are grouped together in a cluster. Now objects belonging to one particular cluster will share some common thread while they would be completely different from objects in other clusters. In this technique visual presentation of data is important, for profiling customers this technique comes in handy.  

Association

This data mining technique is employed to find some hidden relationhip patterns among variables, mostly dependent variables belonging to a dataset. The recurring relationships of variables are taken into account in this process. This comes in handy in predicting customer behavior, such as when they shop what items are they likely to purchase together could be predicted.

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Tracking patterns

This technique is especially useful while sorting out data for the businesses. In this process while working with big datasets, certain trends or, patterns are recognized and these patterns are then monitored to draw a conclusion. This pattern tracking technique could also aid in identifying some sort of anomaly in the dataset that might otherwise go undetected.

Big data is accumulating every day and the more efficiently the datasets get processed and sorted, the better would be the chances of businesses and other sectors be accurate in predicting trends and be prepared for it. The field of data science is full of opportunities now, learning Data science using python training could help the younger generation make it big in this field.

 


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Application of Data Science in Healthcare

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Application of Data Science in Healthcare

In today’s data-driven world,  it is hard to ignore the growing need for data science, as businesses are busy applying data to devise smarter marketing strategies and urging their employees to upgrade themselves. Data Science training is gaining ground as lucrative career opportunities are beckoning the younger generation.

So, it is not surprising that a crucial sector like healthcare would apply data science to upgrade their service. Health care is among one of the many sectors that have acknowledged the benefits of data science and adopted it.

The Healthcare industry is vast and it comprises many disciplines and branches that intercross generating a ton of unstructured data which if processed and analyzed could lead to revolutionary changes in the field.

Here is taking a look at how the industry can benefit by adopting data science techniques

Diagnostic error prevention

No matter what health issues one might have, accurate diagnosing is the first step that helps a physician prescribe treatment procedure. However, there have been multiple cases where a diagnostic error has led to even death. With the implementation of data science technology, it is now possible to increase the accuracy of the procedures as the algorithm sifts data to detect patterns and come up with accurate results.

Medical imaging procedures such as MRI, X-Ray can now detect even tiniest deformity in the organs which were erstwhile impossible, due to the application of deep learning technology.  Advanced models such as MapReduce is also being put to use to enhance the accuracy level.

Bioinformatics

 Genomics is an interesting field of research where researchers analyze your DNA to understand how it affects your health. As they go through genetic sequences to gain an insight into the correlation, they try to find how certain drugs might work on a specific health issue.

The purpose is to provide a more personalized treatment program. In order to process through the highly valuable genome data, data science tools such as SQL are being applied. This field has a vast scope of improvement and with more advanced research work being conducted in the field of Bioinformatics, we can hope for better results.  Researchers who have studied Data science using python training, would prove to be invaluable assets for this specific field.

Health monitoring with wearables

Healthcare is an ongoing process, if you fall ill, you get yourself diagnosed and then get treatment for the health condition you have. The story in most cases does not end there, with the number of patients with chronic health problems increasing, it is evident that constant monitoring of your health condition is required to prevent your health condition from taking a worse hit.  Data science comes into the picture with wearables and other forms of tracking devices that are programmed to keep your health condition in check. Be it your temperature or, heartbeat the sensors keep tracking even minute changes, the data is analyzed to enable the doctors take preventive measures, the GPS-enabled tracker by Propeller, is an excellent case in point.

Faster approval of new drugs

The application of data science is not restricted to only predicting, preventing, and monitoring patient health conditions. In fact, it has reached out to assist in the drug development process as well. Earlier it would take almost a decade for a drug to be accessible in the market thanks to the numerous testing, trial, and approval procedures.

But, now it is possible to shorten the duration thanks to advanced data science algorithms that enable the researchers to simulate the way a drug might react in the body. Different models are being used by the researchers to process clinical trial data, so, that they can work with different variables. Data Science course enables a professional to carry out research work in such a highly specialized field.

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In the context of Covid-19

With the entire world crippling under the unprecedented impact of COVID-19, it is needless to point out that the significance of data science in the healthcare sector is only going to increase. If you have been monitoring the social media platforms then you must have come across the #FlattenTheCurve.

The enormity of the situation and erroneous data collection both have caused issues, but, that hasn’t deterred the data scientists. Once, the dust settles they will have a mountainous task ahead of them to process through a massive amount of data the pandemic will have left behind, to offer insight that might help us take preventive measures in the future.

The field of data science has no doubt made considerable progress and so has the field of modern healthcare. Further research and collaboration would enable future data scientists to provide a better solution to bolster the healthcare sector.

 


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Top 7 Data Science Platforms to Choose From in 2020

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Top 7 Data Science Platforms to Choose From in 2020

Lack of collaboration between team members could be a frustrating experience as could be spending time maintaining your models after deploying them.

These reasons among others could mean the need for adopting data science platforms and having to choose the right platform from a host of available packages in the market.

“Various organizations keep floating data science platforms to simplify machine learning workflows. However, in the ever-changing data science landscape, only a few draw the attention of practitioners,” says a report.

Here is a list of top 7 data science platforms available for use in 2020.

Databricks

“Built by the founder of Apache Spark, Databricks provides a unified analytics platform that allows data scientists to manage end-to-end machine learning workflows.

The one-size-fits-all platform not only enables practitioners to explore, visualize and build superior machine learning models, but also allows them to scale it quickly with the help of collaboration.”

DataRobot

DataRobotassists companies to automate the workflows of machine learning through its feature-rich solutions and it constantly strives to enhance its platform by either acquiring various companies, or by developing in-house solutions.

“Apart from assisting the regular analytics workflows”, DataRobot is among the best in the AutoML arena.

Apache Spark

“Apache Spark is an open-source unified analytics engine for large-scale data processing and analyzing. It is similar to HadoopMapReduce; it works on cluster computing, but due to exceptional speed – which is believed to be 100x faster in memory and 10x faster on disk than Hadoop – it has become popular among data scientists.”

Dataiku

This is yet another reputed enterprise AI and machine learning platform that “helps businesses in minimizing data processes to expedite the development of machine learning-based solutions”.

The platform helps companies in bringing together data analysts, engineers, and scientists to achieve shared goals through collaboration. “It also provides instant visual and statistical feedback on model performance to manage models’ lifecycle effectively”.

IBM Cloud Pak for Data

“Built on Red Hat OpenShift container platform, IBM Cloud Pak for Data is a fully-integrated AI platform to meet the changing needs of enterprises. It allows data scientists to unlock insights and eliminate data silos quickly.

The platform has a high degree of enterprise readiness and delivers business value by enabling practitioners to integrate with other platforms using APIs.”

Alteryx

“Alteryx is a self-service analytics platform that can be utilized across organizations to democratize data. The platform caters to every need of analytics professionals, such as business intelligence, data analyst, data scientist, and non-experts to assist them in quickly solving business problems. It supports analytics modelling without code and advanced modelling with algorithms.”

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TIBCO

TIBCO Software acts as a foundation for digital innovation for data-driven companies. “Integration among platforms has been one of the longest standing predicaments for organizations.”

“Thus, TIBCO offers a suite of products like Connect, API-Led Integration, Data Fabric, Unify, Data Science & Streaming, and more, to eliminate challenges for a streamlined data science workflow.”

For more on this do peruse the DexLab Analytics website today. DexLab Analytics offers the best Alteryx Training in Delhi NCR.

 


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Why Learning Python is Important for Data Scientists Today

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Why Learning Python is Important for Data Scientists Today

Data Science is the new rage and if you are looking to make a career, you might as well choose to become a data scientist. Data Scientists work with large sets of data to draw valuable insights that can be worked upon. Businesses rely on data scientists to sieve through tonnes of data and mine out crucial information that becomes the bedrock of business decisions in the future.

With the growth of AI, machine learning and predictive analytics, data science has come to be one of the favoured career choices in the world today. It is imperative for a data scientist to know one of more programming languages from any of those available – Java, R, Python, Scala or MATLAB.

However, Data Scientists prefer Python to other programming languages because of a number of reasons. Here we delve into some of them.

Popular

Python is one of the most popular programming languages used today. This dynamic language is easy to pick up and learn and is the best option for beginners. Secondly, it interfaces with complex high performance algorithms written in Fortran or C. It is also used for web development, data mining and scientific computing, among others.

Preferred for Data Science

Python solves most of the daily tasks a data scientist is expected to perform. “For data scientists who need to incorporate statistical code into production databases or integrate data with web-based applications, Python is often the ideal choice. It is also ideal for implementing algorithms, which is something that data scientists need to do often,” says a report. 

Packages

Python has a number of very useful packages tailored for specific functions, including pandas, NumPy and SciPy. Data Scientists working on machine learning tasks find scikit-learn useful and Matplotlib is a perfect solution for graphical representation and data visualization in data science projects.

Easy to learn

It is easy to grasp and that is why not only beginners but busy professionals also choose to learn Python for their data science needs. Compared to R, this programming language shows a sharper learning curve for most people choosing to learn it.

Scalability

Unlike other programming languages, Python is highly scalable and perceptive to change. It is also faster than languages like MATLAB. It facilitates scale and gives data scientists multiple ways to approach a problem. This is one of the reasons why Youtube migrated to Python.

Libraries

Python offers access to a wide range of data science and data analysis libraries. These include pandas, NumPy, SciPy, StatsModels, and scikit-learn. And Python will keep building on these and adding to these.  These libraries have made many hitherto unsolvable problems seem easy to crack for data scientists.

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Python Community

Python has a very robust community and many data science professionals are willing to create new data science libraries for Python users. The Python community is tight-knit one and very active when it comes to finding a solution. Programmers can connect with community members over the Internet and Codementor or Stack Overflow.

So, that is why data scientists tend to opt for Python over other programming languages. This article was brought to you by DexLab Analytics. DexLab Analytics is premiere data science training institute in Gurgaon.

 


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Dexlab Analytics Starts National Level Training On Data Analysis Using OpenAir package of R

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Dexlab Analytics Starts National Level Training On Data Analysis Using OpenAir package of R

From Saturday, 6th June 2020, a team of senior consultants at DexLab Analytics has been conducting a national level training for more than 40 participants who are research scholars, MPhil students and professors from colleges like IIT, CSIR, BHU and NIT, among others. This one of a kind, crowd-funded training is being conducted on “Environment Air pollution Data Analysis using OpenAir package of R”.

The training is a result of the lockdown wherein DexLab Analytics is working towards its upskilling initiatives for professionals and subject matter experts across India. The training is being conducted in DexLab Analytics’TraDigital format – real time, online, classroom styled, instructor-led training.

The attendees will be taking up these interactive classes from the safety and comfort of their homes. They will be getting assignments, learning material and recordings virtually.

The one-month-long training will be conducted in R Programming, Data Science and Machine Learning using R Programming from the perspective of Environmental Science. DexLab Analytics is conducting this training module in line with the tenets of ‘Atmanirbhar India’.

Data Science Machine Learning Certification

DexLab Analytics is a leading data science training institute in India with a vast array of state-of-the-art analytics courses, attracting a large number of students nationwide. It offers high-in-demand professional courses like Big Data, R Programming, Python, Machine Learning, Deep Learning, Data Science, Alteryx, SQL, Business Analytics, Credit Risk modeling, Tableau, Excel etc. to help young minds be data-efficient. It has its headquarters in Gurgaon, NCR.

 

For more information, click here – 

www.prlog.org/12825521-dexlab-analytics-starts-national-level-training-on-data-analysis-using.html

 


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Mr Debuka is Key Speaker at EIILM’s Webinar

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Mr Debuka is Key Speaker at EIILM’s Webinar

DexLab Analytics is proud to announce that its CMO, Vivek Debuka, was the Key Speaker at a webinar hosted by the Eastern Institute for Integrated Learning in Management (EIILM), Kolkata on “Changing Trend in Business in the Post COVID-19 World”.

The webinar was held on 30th May, 2020 from 5pm – 6pm. Students of the Eastern Institute for Integrated Learning in Management, the chairman and director of the EIILM Dr R P Banerjee said, were excited and eager to attend the webinar, especially because the topic was an emerging one and relevant to their corporate career goals.

On May 27, EIILM posted a Facebook post that read – “EIILM’s initiative for enriching young minds with post COVID-19 business trends!!!! The Covid era has brought about a lot of uncertainties that have resulted in a new thought process in the ever-changing world of business. To orient our budding managers with the dynamic business trends, EIILM – KOLKATA Family has scheduled a Webinar on 30 May 2020, from 5-6 pm under the title “Changing Trend in Business in the Post Covid 19 World”.

Data Science Machine Learning Certification

DexLab Analytics is a leading data science training institute in India with a vast array of state-of-the-art analytics courses, attracting a large number of students nationwide. It offers high-in-demand professional courses like Big Data, R Programming, Python, Machine Learning, Deep Learning, Data Science, Alteryx, SQL, Business Analytics, Credit Risk modeling, Tableau, Excel etc. to help young minds be data-efficient. It has its headquarters in Gurgaon, NCR.

 
For more information click on the link here – www.prlog.org/12824488-dexlab-analytics-cmo-was-key-speaker-at-eiilm-webinar.html
 


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93% Indian Professionals Benefitting From E-Learning During Lockdown: Linkedin

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93% Indian Professionals Benefitting From E-Learning During Lockdown: Linkedin

The Covid-19 pandemic has struck India like it has scores of countries across the world. As of May 27, over 1,51,000 Indians have been tested positive for the novel virus and over 4000 people have died due to the contagious disease. India has been under lockdown for over two months now in an attempt at abating the spread of the virus due to movement and contact.


 

With all offices closed and work from home decreed across numerous sectors of the economy, professionals have been forced to adapt to a new mode of work and training. With more time on hand since they are working from home, professionals are upgrading their skills by taking up online training modules and classes. A recent LinkedIn survey throws light on this phenomenon.

LinkedIn’s Work Force Confidence Index

India’s foremost social networking site that helps individuals network with professional peers and find jobs and appointments has conducted a survey called Work Force Confidence Index. As per the survey conducted between April 27 and May 3, “India’s professionals are logging learning hours for not just knowledge acquisition but also to increase productivity. About half of respondents from mid-market firms joined courses that help them manage time better, improve prioritisation or stay organised”.

93% Indian Professionals Benefitting From E-Learning During Lockdown: Linkedin

93% respondents to upskill online in next two weeks

According to LinkedIn News India, 1040 professionals were surveyed by LinkedIn and 93% of them said “their time spent on e-learning will either increase or remain the same over the next two weeks”. Moreover, 60% of the respondents of which 74% were from the engineering domain said e-learning was a conduit to furthering industry knowledge. “Advancing in one’s career was a driver for 57% of all respondents and 3 in 10 active job seekers undertook e-learning to make a career pivot,” said LinkedIn News India.

What respondents learnt

Of the respondents, 45% said they hoped to learn to collaborate with peers through online learning in lockdown. Also, 43% said they wished to learn to manage time and prioritise and stay organised. Moreover, 40% said they hoped to learn something unrelated to work through online platforms. Becoming a leader and managing personal finances were pegged at 37% and 32% respectively by the study as goals and 24% said e-learning could actually lead to a change in career paths for them.

Advantages of e-learning

Travelling to work and back is taxing and time consuming. When you are working from home, you save on energy and time that can be used for something productive like e-learning training modules. They are easy on the pocket, accessible from absolutely anywhere you are and convenient to absorb and retain information and new things learnt. Moreover, there is a large online community to help you out with study material and guidance.

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There are many popular e-learning courses in India, especially those around data science and artificial intelligence. DexLab Analytics is a premier credit risk modeling training institute that also trains professionals in artificial intelligence, machine learning and data science. This article was brought to you by DexLab Analytics.

 


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The Data Science Life Cycle

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The Data Science Life Cycle

Data Science has undergone a tremendous change since the 1990s when the term was first coined. With data as its pivotal element, we need to ask valid questions like why we need data and what we can do with the data in hand.

The Data Scientist is supposed to ask these questions to determine how data can be useful in today’s world of change and flux. The steps taken to determine the outcome of processes applied to data is known as Data Science project lifecycle. These steps are enumerated here.

  • Business Understanding

Business Understanding is a key player in the success of any data science project. Despite the prevalence of technology in today’s scenario it can safely be said that the “success of any project depends on the quality of questions asked of the dataset.”One has to properly understand the business model he is working under to be able to effectively work on the obtained data.

  • Data Collection

Data is the raison detre of data science. It is the pivot on which data science functions. Data can be collected from numerous sources – logs from webservers, data from online repositories, data from databases, social media data, data in excel sheet format. Data is everywhere. If the right questions are asked of data in the first step of a project life cycle, then data collection will follow naturally.

  • Data Preparation

The available Data set might not be in the desired format and suitable enough to perform analysis upon readily. So the data set will have to be cleaned or scrubbed so to say before it can be analyzed. It will have to be structured in a format that can be analyzed scientifically. This process is also known as Data cleaning or data wrangling. As the case might be, data can be obtained from various sources but it will need to be combined so it can be analyzed.

For this, data structuring is required. Also, there might me some elements missing in the data set in which case model building becomes a problem. There are various methods to conduct missing value and duplicate value treatment.

“Exploratory Data Analysis (EDA) plays an important role at this stage as summarization of clean data helps in identifying the structure, outliers, anomalies and patterns in the data.

These insights could help in building the model.”

  • Data Modelling

This stage is the most, we can say, magical of all. But ensure you have thoroughly gone through the previous processes before you begin building your model. “Feature selection is one of the first things that you would like to do in this stage. Not all features might be essential for making the predictions. What needs to be done here is to reduce the dimensionality of the dataset. It should be done such that features contributing to the prediction results should be selected.”

“Based on the business problem models could be selected. It is essential to identify what is the task, is it a classification problem, regression or prediction problem, time series forecasting or a clustering problem.” Once problem type is sorted out the model can be implemented.

“After the modelling process, model performance measurement is required. For this precision, recall, F1-score for classification problem could be used. For regression problem R2, MAPE (Moving Average Percentage Error) or RMSE (Root Mean Square Error) could be used.”The model should be a robust one and not an overfitted model that will not be accurate.

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  • Interpreting Data

This is the last and most important step of any Data Science project. Execution of this step should be as good and robust as to produce what a layman can understand in terms of the outcome of the project.“The predictive power of the model lies in its ability to generalise.” 

 


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