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Tag: Data Science Training

What Is A Cluster Analysis With R? How Can You Learn It From A Scratch?

Posted on by Imarticus Learning

What is Cluster analysis?

Cluster means a group, and a cluster of data means a group of data that are similar in type. This type of analysis is described more like discovery than a prediction, in which the machine searches for similarities within the data.

Cluster analysis in the data science career can be used in customer segmentation, stock market clustering, and to reduce dimensionality. It is done by grouping data with similar values. This analysis is good for business.

Supervised and Unsupervised Learning-

The simple difference between both types of learning is that the supervised method predicts the outcome, while the unsupervised method produces a new variable.

Here is an example. A dataset of the total expenditure of the customers and their age is provided. Now the company wants to send more ad emails to its customers.

library(ggplot2)

df <- data.frame(age = c(18, 21, 22, 24, 26, 26, 27, 30, 31, 35, 39, 40, 41, 42, 44, 46, 47, 48, 49, 54),

spend = c(10, 11, 22, 15, 12, 13, 14, 33, 39, 37, 44, 27, 29, 20, 28, 21, 30, 31, 23, 24)

)

ggplot(df, aes(x = age, y = spend)) +

geom_point()

In the graph, there will be certain groups of points. In the bottom, the group of dots represents the group of young people with less money.

The topmost group represents the middle age people with higher budgets, and the rightmost group represents the old people with a lower budget.

This is one of the straightforward examples of cluster analysis. 

K-means algorithm

It is a common clustering method. This algorithm reduces the distance between the observations to easily find the cluster of data. This is also known as a local optimal solutions algorithm. The distances of the observations can be measured through their coordinates.

How does the algorithm work?

  1. Chooses groups randomly
  2. The distance between the cluster center (centroid) and other observations are calculated.
  3. This results in a group of observations. K new clusters are formed and the observations are clustered with the closest centroid.
  4. The centroid is shifted to the mean coordinates of the group.
  5. Distances according to the new centroids are calculated. New boundaries are created, and the observations move from one group to another as they are clustered with the nearest new centroid.
  6. Repeat the process until no observations change their group.

The distance along x and y-axis is defined as-

D(x,y)= √ Summation of (Σ) square of (Xi-Yi). This is known as the Euclidean distance and is commonly used in the k-means algorithm. Other methods that can be used to find the distance between observations are Manhattan and Minkowski.

Select the number of clusters

The difficulty of K-means is choosing the number of clusters (k). A high k-value selected will have a large number of groups and can increase stability, but can overfit data. Overfitting is the process in which the performance of the model decreases for new data because the model has learned just the training data and this learning cannot be generalized.

The formula for choosing the number of clusters-

Cluster= √ (2/n)

Import data

K means is not suitable for factor variables. It is because the discrete values do not produce accurate predictions and it is based on the distance.

library(dplyr)

PATH <-“https://raw.githubusercontent.com/guru99-edu/R-Programming/master/computers.csv”

df <- read.csv(PATH) %>%

select(-c(X, cd, multi, premium))

glimpse(df)

Output:

Observations: 6,259

Variables: 7

$ price  <int> 1499, 1795, 1595, 1849, 3295, 3695, 1720, 1995, 2225, 2575, 2195, 2605, 2045, 2295, 2699…

$ speed  <int> 25, 33, 25, 25, 33, 66, 25, 50, 50, 50, 33, 66, 50, 25, 50, 50, 33, 33, 33, 66, 33, 66, …

$ hd     <int> 80, 85, 170, 170, 340, 340, 170, 85, 210, 210, 170, 210, 130, 245, 212, 130, 85, 210, 25…

$ ram    <int> 4, 2, 4, 8, 16, 16, 4, 2, 8, 4, 8, 8, 4, 8, 8, 4, 2, 4, 4, 8, 4, 4, 16, 4, 8, 2, 4, 8, 1…

$ screen <int> 14, 14, 15, 14, 14, 14, 14, 14, 14, 15, 15, 14, 14, 14, 14, 14, 14, 15, 15, 14, 14, 14, …

$ ads    <int> 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, …

$ trend  <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…

Optimal k

Elbow method is one of the methods to choose the best k value (the number of clusters). It uses in-group similarity or dissimilarity to determine the variability. Elbow graph can be constructed in the following way-

1. Create a function that computes the sum of squares of the cluster. 

kmean_withinss <- function(k) {

cluster <- kmeans(rescale_df, k)

return (cluster$tot.withinss)

}

2. Run it n times

# Set maximum cluster

max_k <-20

# Run algorithm over a range of k

wss <- sapply(2:max_k, kmean_withinss)

3. Use the results to create a data frame

# Create a data frame to plot the graph

elbow <-data.frame(2:max_k, wss)

4. Plot the results

# Plot the graph with gglop

ggplot(elbow, aes(x = X2.max_k, y = wss)) +

geom_point() +

geom_line() +

scale_x_continuous(breaks = seq(1, 20, by = 1))

What Are The Resources to Learn Data Science Online?

Posted on by Imarticus Learning

What is Data Science?
In the modern digital era, data is at the heart of every business that relies on the use of technological solutions to boost customer experience and increase revenue. The decision-making process has changed after the advent of data science. Businesses no longer work on assumption; they are using complex data analysis to obtain valuable insights about the market and consumers. So what exactly is data science and how does it work to further business objectives?

Well, data science can be simply explained as a discipline that deals with data collection, structuring and analysis. It involves the use of the scientific process and algorithms to obtain valuable insights from seemingly irrelevant pieces of information. Big data is at the centre of data science. Let’s delve deeper into why you should consider learning data science.

Why Learn Data Science?

The demand for data science professionals is ever increasing as more and more companies are deploying data science to obtain deeper insights.

Data Science Course OnlineThe demand for data science course online is also growing as more individuals are lured in towards the lucrative career prospects offered by this industry. There are numerous reasons to learn data science in the contemporary landscape.

The first and foremost is the outstanding remuneration offered to data science professionals. This is partly because data science is still in its nascent stage and there is a scarcity of trained professionals in this industry.

However, the demand for data science professionals by companies is on an upward trend.

 

In addition to this, the role played by data science professionals is very crucial for businesses as it involves analysing valuable company data to obtain insights and make predictions regarding the market.

Let’s explore how you can easily get trained for data science online.

Resources to Learn Data Science Online
Online learning is the new norm, the benefits of this method of learning is enormous. Moreover, the online courses are designed in such a way that it caters to specific training needs of individuals and there is no irrelevant content included in the courses. It is also feasible for people who are already working at a job and have limited time to learn a new subject. Here are a few resources that can help you learn data science online with ease and in a limited budget.

Google’s Machine Learning Crash Course

The machine learning technology is being extensively used by companies to cater to a growing audience base. Google’s Machine Learning Crash Course is designed for everyone; it doesn’t require you to have any prerequisite knowledge regarding the subject. Even people who have some knowledge in the field can opt for this course as it focuses on important concepts like loss functions, gradient descent, etc.

In addition to this, you will also learn about presenting algorithms from linear regression models to neural networks. The course learning materials include exercises, readings, and notebooks with actual code implementation using Tensorflow.

In addition to this crash course, you will also have access to a plethora of learning materials on data science and AI. These learning materials include courses, Practica, Guides and Glossary.

Imarticus Learning’s Data Science Prodegree

If you are looking to make a professional career in the field of data science then the data science course offered by Imarticus Learning is surely the best way to learn data science. The best thing about this course by Imarticus is that the knowledge partner for this course is KPMG.

This data science course takes a comprehensive approach towards learning data science and covers topics such as R, Python, SAS Programming, Data visualisation with Tableau, etc.

Data Science And Machine Learning Course with iHUB DivyaSampark @IIT Roorkee

Data science is a competitive field and to be successful you need to master the foundational concepts of data science. Imarticus Learning has created a 5-month data science program with iHUB DivyaSampark @IIT Roorkee. It will equip you with the most in-demand data science skills and knowledge that will help you to pursue a career as a data scientist, business analyst, data analyst and data manager. It features a 2-day campus immersion program at iHUB Divyasampark @IIT Roorkee and is delivered by top IIT faculty through live online training. Through this program, you will also get an opportunity to showcase your startup idea and get funding support.

In addition to this, the course trains individuals using industry sneak peeks, case studies and projects. The capstone projects allow individuals to work on real-world business problems in the guidance of expert project mentors. Upon the successful completion of this course, you will also receive a certification by Imarticus learning in association with Genpact. In addition to all this, you will receive interview preparation guidance and placement assistance.

 

What Skills Are Needed to Be A Data Scientist?

Posted on by Imarticus Learning

A career in data science is highly attractive owing to its payment structure, job opportunities, and future career prospects. There is any number of Data scientist courses that you can find and that makes you qualify for the job.
The major criteria for this career are a few skills that one can easily master through the right path.

These skills could very well be different from any former experience in the career thus far. Developing these skills will help the recruiters to identify you as the best option for what they are looking for!

Programming language
A strong knowledge base of any major programming languages such as Python, R, or SQL is the foremost requirement to be an expert in data science. No matter what the company or the job profile is, this is one field of expertise that is non-negotiable.

Statistics
Statistics hold more value in data science since it helps to deal with the raw data of the companies. It helps with the evaluation, designing, and making decisions in the later stages.

Deep learning
This machine learning technology enables computers to work like the human brain.

Data Science CourseAn enormous amount of data is managed through computing power to make it possible. A career in data science, especially that in the automobile and AI industry requires this particular skill.

Working with unstructured data
Data science is mainly about the gigantic amount of data from various sources. The vast majority of this data is in a raw and unstructured format. A skilled data analyst can easily go through them to find and identify what they are looking for to make it useful.

Appetite for problem-solving
Simply looking at the data is not what makes the analyst skillful. It also calls for the right appetite to identify the problems underneath and finding the ideal solution as well. For which the analyst needs to have the drive for problem-solving and look in the right areas.

Data visualization
This is the skill that enables a data scientist to identify and decode the raw data into an identifiable visual to use it to convey. This skill enables the analyst to see what the data is useful for with the help of the various data visualization tools.

Communication skill
It comes next to the visualization part. The visualized data needs to be explained in a simple and well-constructed plan to the stakeholders. AT this juncture, the analyst must have strong communication skills to convey the key points and make them believe in the same. Polishing communication skills would be an added advantage to improve career prospects.

Familiarity with data science tools
Data science involves various types of tools to help with data processing. An analyst must have a fairly good idea about the working of most of the tools. Since each type of data requires different tools, it is highly imperative to be on familiar terms with these tools. Most of them are pre-programmed, so you just need to know how to use them in the proper way.

Intuition
Last but not the least, having a strong intuition on what to look for, how to use it, and which tool needs when to get the best result out of the data analysis happens to be the strongest point of being a successful data scientist.

Conclusion
Most of these skills are covered in the Online data scientist course in India available from various sources online or otherwise. What needs more work would be on soft skills which also have an equally important role in a successful career. A career in data science does not have refined eligibility criteria, instead, it mainly depends on these acquired skills.

Top Career Options in Data Science!

Posted on by Imarticus Learning

Data Science is an emerging and yet established interdisciplinary filled that makes use of objectively led processes, methodology, systems, and carefully curated algorithms to study data. This file is very close to and often overlaps with Big Data and Data Mining.

By careful study of the Data Science Course, this field aims to extract important information and patterns that can be used for a number of decision making, information gathering, and data collection tasks.

Where is Data Science Used? 

Data Science is being used by a myriad of fields ranging from state-sponsored departments, the police, military, private companies, NGOs, marketing experts, researchers, and customer service support groups around the world. Most recent and successful technologies such as face recognition are a product of data science innovation. Cookies that online retail stores and online publications use are based on this field too. Data science has entered almost every aspect of our digital lives in a short span of time.

What are some of the jobs that Data Science has?

Here is a list of top Data Science Career Options in Data Science that are shaping our future:

  • Data Scientist: This is a highly sought after job in the field of Data Science. A data scientist is expected to study all big and small data that has been gathered. They are also supposed to form the recommender systems and organize the data for analysis. All major corporations like Facebook, Google, Microsoft, Twitter, etc. employ data scientists. This job is better suited for people who are good at mathematics and coding.
  • Machine learning engineer: A machine learning engineer is entrusted with the job of making data funnels that aid in software creation. They also construct the appropriate and suitable algorithms needed for problem-solving. The machine learning engineers study the systems and its prototypes by running regular tests. They experiment with different problem-solving techniques and modify the current operating steps to improve the current methodology and quality of work. Machine learning engineers are highly paid.
  • Business Analyst: A business analyst tests data by keeping in mind the requirements of the business house it is serving. One does not have to be specifically from a technical field to perform this job. A business analyst has knowledge about industries like telecom, finance, logistics, marketing, and retail.These people are well informed about government and legal policies related to financial technology. A business analyst helps a company find out what information they need to enhance the company’s consumer behavior, marketing strategies, and relationship with its customers.
  • Data Analyst: Like the name suggests data analysts are responsible for primarily web tracking, testing, and operating big and large data sets. They use a mix of statistical tests and interdisciplinary methodology made up of qualitative and quantitative tools to study big data.They have to pick relevant patters and form conclusions based on a set of figures available. A good data analyst is equipped with the number of fact-finding and statistical tests that can be applied to a varied set of data packs depending on the availability of the information. A capable data analyst will be perceptive and informed about which tool and method have the best probability of revealing the most reliable information.

Conclusion

Data Science is one of the most expansive and quickly growing fields in the world. There has been a steep rise in several Data Science Coursetakers in the last few years. The reason for this recent increase in popularity is the number of jobs that have emerged in this area. Since data science is multidisciplinary, people from different subjects and work fields can collaboratively work in it.

What is The Salary Of A Data Scientist?

Posted on by Imarticus Learning

One of the biggest drivers of data science as a career in India is its earning ability. In India alone, the average annual pay of a data scientist (analyst) is upwards of INR 8 lakh. This can definitely vary depending upon the location, the type of the job, and the employer.

While you can improve the chances of earning a higher salary by signing up for a data science course, it is a good idea to look at how the remuneration looks like through the cycle of a data science career.

Hierarchy of Salaries of a Data Scientist

This is a quick look at how your salary will grow as you gain more experience and get trained for a data science career.

  • Beginner – An entry-level data scientist from a reputed institution can look at earning upwards of INR 5 lakh a year. This is a better salary compared to those that engineering graduates receive in India. Here, as a beginner, your goal should be to gain more hands-on experience in your specific field to demand a better salary. For example, a data science training course can improve your chances by 2x.
  • Analyst With 1-4 Years of Experience – Although there is a possibility that your data science salary can touch the INR 10 lakh mark in this period, it will depend on your projects and overall work experience. This is a good time to shift from your existing employer and look at greener pastures.
  • Professional – This is when you achieve more than 10 years of experience. Here you can dictate a salary in the range of INR 15 lakhs to INR 25 lakhs. With several credentials and projects in your name, you can really carve out a prosperous life for yourself and your family. This is also where you can think of spending some time on research.

Just remember that when you start off, make sure you are well trained for a data science career. As a fresher, your academic qualifications will play a vital role during interviews. A data science training course that will help you choose a specialty and make it a focal point is the way to go.

Promotions and Salary Hikes in Data Science

As with every other professional field, a promotion in a data science career is entirely dependent on performance. However, a salary hike of 30% to 50% can be expected. Again, this will be influenced by how well you perform in your role, but given that skilled data scientists are hard to find in India, you can be optimistic.

In any case, if you don’t feel you are professionally growing in your current company, you can always look at upskilling or switching jobs. A data science training course can make you a potential candidate then.

As noted above, getting trained for a data science career can boost your earning and promotion capabilities. While data scientist intrinsically is a well-paying job, a training course in the field will do no harm.

Tableau: Accelerating Decision-making with the Power of Visual Analytics!

Posted on by Imarticus Learning

Tableau is one of the most frequently used data analytics tools. It is used for data visualization where the data is represented in a pictorial or graphical form. The raw data is converted into an understandable format by Tableau which can be further used for data analytics.

Firms use Tableau to understand the data and to use data analytics for empowering their business. Tableau helps in decision-making via forecasting, analytics, risk assessment, etc.

Let us see more about Tableau and how it helps in decision making & data visualization.

Importance of Visual Analytics

Visual analytics is the analytical reasoning of data via interactive visual interfaces which in this case is Tableau. Visual analytics helps in understanding the data better, finding outliers in the dataset, discovering insights, etc.

You can identify new opportunities for your business if you are visualizing your data via a good platform like Tableau. The benefits of using Tableau for visual analytics are as follows:

  • Tableau has excellent visualization capabilities and it helps in converting unstructured data into absolute logical results that are interactive. It is far better and powerful than its equivalent tools available in the market.Data Visualization
    The easy-to-use interface of Tableau provides data analysts to work faster and better. The drag-and-drop way of arranging unstructured data into diagrams and graphs makes it easier for beginners.Tableau is powerful and provides high performance of big data sets too. Beginners can easily complete Tableau training in less time and can get fluent in using it.
  • You can create a unified dashboard on Tableau where you can connect to multiple data sources. You can connect your dashboard with Hadoop, SAP, DB, etc., and can visualize data better.
  • There are a lot of Tableau users throughout the globe and one can find a helpful Tableau community on online forums. Tableau also provides a mobile application through which you can keep your visualized data at your fingertips.

How Tableau Accelerates Decision Making?

Tableau helps in visualizing and analyzing data. The structured data can be used for risk identification & management, increasing ROI, business forecasting, etc. You are well informed about your firm’s situation via data analytics and get to know about the upcoming market trends. You access the risk involved in any new opportunity via data analysis and then obtain it if is going to boost your business.

Tableau training in data visualization

Data analysis introduces clarity in your organization with a data-driven approach to obtain business objectives. Decision-making is hugely impacted via the use of data visualization tools and you will stay ahead of other firms in the market.

One can easily create and share analytics reports to your employees via Tableau, giving a sense of clarity. You can also use customer data to provide better services to them in the future. One can complete Tableau training online to understand how to use it.

Conclusion

Tableau is a powerful data visualization tool that can boost your business with better market predictions & risk assessment. Recent innovations in data analysis due to AI & machine learning have taken data analysis to a next level. You can learn more about data analysis via analytics courses available online. Start using Tableau for your business now!

Why is Data Science a Good Career in 2021?

Posted on by Imarticus Learning

Being a data scientist is only growing in demand over 2021 and is showing no signs of slowing down. It is estimated that around 11.5 million jobs in data science will be created by 2026 in the US. But, why is that the case? This article seeks to answer that very question.

  1. Use in Companies

Due to the ever-growing base of Big Data, every company is looking to utilize all available information to have a massive competitive edge.

Data Science CareerA data science career under a company is a frontier-field that finds new ways to better one’s products and services after utilizing past stores of information and/or case studies.

This work hence involves finding various avenues of data and finding new ways of processing and drawing conclusions from that data.

  1. Use in Studies

Being a form of study that is still in its nascent stages, a data science career may not be motivated by finding profit for a certain industry but also increasing the ambit of human knowledge. One might also work on designing a data science course from others to learn from.

  1. Proper Pathway

While being a data scientist requires a lot of work, the exact path to such a goal has been charted time and time again. There is a great degree of resources available now to become proficient in various aspects related to the data sciences. Other than doing a basic data science course, one may partake in learning various related fields like programming and big data processing from various online platforms (e.g. Imarticus learning).

  1. Demand Doesn’t Slack

The demands for data sciences have also increased due to the new atmosphere generated by Covid-19 and the near-worldwide lockdown because of it.

Data Science Roles

It has been studied that 50% of the data science organization showed no slow-down and have seen growth. This requires one to find new ways to collect data, as well as use that data to aid in multiple projects. These may involve helping set up new modes of businesses, and helping older businesses change their plans to suit their new circumstances. Furthermore, it may aid in improving a range of services on a global level.

  1. Diverse Skillsets

It is easy to switch into being a data scientist incorporating your present skillset. Whatever your present occupations and/or interests may be, it can lend an avenue to collecting data on that specific domain.

Data Science TrainingThey can complement these skills with learning standard data sciences’ skills. Former data analysts may also expand on their present sphere of knowledge to become data scientists, with relative ease.

  1. An Expanding Field

In 2021, a lot of past data science models are up to open-source scrutiny. Hence, even in this new field of human knowledge, one can have a sizable understanding of multiple avenues of collecting and processing data. Their entry into data sciences will work to expand on this field of knowledge.

In conclusion, one can see that it is indeed highly fruitful to be a data science in this present day and age. One can channel his/her present skillset into this occupation as well and aid a burgeoning field of human growth and knowledge.

Python Developer Salary in Terms of Job Roles

Posted on by Imarticus Learning

What is Python?

The second most liked Programming language in the world, Python is one of the widely used term in the web-development world.

Who are Python Developers?

The web-developers who design and code the software applications with the help of Python language are referred to as Python Developers.

Roles and responsibilities of Python Developers

 Python Developers as Data Scientists

Major businesses in today’s world require tools and skilled people for the data-related tasks such as data collection, data cleaning and processing.

Python Programming Course with Data ScienceData Scientists are the programmers who do these tasks for the organizations. Data Scientists gather a large quantity of data and convert it into a useful form, followed by recognizing data-analytics solutions for organizational growth.

Data Scientists encourage the data-driven approach in organizations to deal with complex business problems.

 Artificial Intelligence

In AI Python Developers create and implement the required Machine Language algorithms. They analyze the success and failure of the algorithm and rank them according to their performance for future use. Training and Retraining ML algorithms is one of the key tasks performed by Python Developers.

Salaries of Python Developers in India

The changing focus of the organizations on data-driven solutions is resulting in a manifold increase in the salaries of the Python Developers. In the coming years the demand for AI-skilled people will increase, and hence the salaries.

Major IT giants like Google, YouTube, Amazon etc. are adopting Python-driven systems and hence, manifold increases in the salaries of the Python Developers.

Python Programming Course with Data scienceThe entry level salary of a Data Scientist is approximately INR 500,000 per annum (Source: payscale.com) Data scientists with 5-9 years of work experience gets +100% more salary in India. With the experience and refinement of the skill, the salary of Data scientists shows exponential growth.

 An entry-level engineer who develops the ML Algorithms earns the average salary of approximately INR 700,000 annually. With further experience and refinement of the skills, the average salary of the Data Scientist shows exponential growth.

Factors affecting the Salaries of Python Developers

Job location: Considering the increasing demand of Python Developers, not only in India, the faster salary growth is visible in UK and in US.

Location

 Approximate Average Salary in INR

Gurgaon

 700,717

Bangalore

669,787

Delhi

600,000

Mumbai

579,728

Chennai

540,131

Hyderabad

 475,000

UK Python Developer annual salary

£67,000

US Python Developer annual salary

 $117,000

Experience:

Python Developer

Approximate Salaries in INR

Entry Level Python Developer

427,293

Med-Level Python Developer

 909,818

Experienced Python Developer

 1,150,000

Refined Skills:

Mere understanding of the Python is of no use, till is integrated with the problems and solutions. How one uses the well-known Python tools define the person’s skill set, which is a determiner of the salary.

Job Role:

Python Developer

Approximate Average Salary in INR

Data Scientists

700,000

ML Engineer

 670,000

DevOps Engineer

660,000

Software Engineer

500,000

Web Developer

 300,000

 

Python Programming Course with Data ScienceShould know more interesting things about Python programming training and Python career.

 

Which Career Is More Promising: Data Scientist or Software Developer?

Posted on by Imarticus Learning

As per a study, about 2.5 quintillion bytes of data are produced every day. The number is about to increase by many folds in the future. The pace at which data is being generated demands more data science professionals. That’s the reason data science jobs are now regarded as one of the highest-paid jobs across the globe. And, people are joining data science training to explore a career in data.

As, firms are hiring employees who can build technical applications, and collect, analyse and predict data. That’s the reason students are enroling in data science training courses that are best and give job assurance.

Companies are keen to employ data scientists, data analysts, web developers, full-stack developers, and much more technology professionals. According to Imarticus Learning and Analytics India Magazine’s research, there will be a 30.1% increase in data science and analytics jobs in 2022 compared to 2021.

However, more competition in Information Technology requires software career enthusiasts to learn the latest technology for survival. The primary responsibility of data scientists is business analytics. They also work on constructing data and software products using algorithms. The demand for data scientists is high due to the shortage of skilled people for start-ups and corporates. Global companies are searching for professional data scientists to up their game in this competitive world.

Who is a data scientist?

Data scientists collect and interpret large amounts of structured and unstructured information. They analyse, process, and model data before interpreting the findings to develop actionable plans for businesses. The data scientist uses computer science, statistics, and mathematics for data interpretation.

Who is a software developer?

Software developers are the thinkers and doers behind computer programs of all kinds. While some software developers specialise in a single program or application development, others build massive networks or underlying systems that trigger and power other programs. As a result, there are two types of developers: applications software developers and systems software developers.

The approach of data scientists and software developers

To use and organise data in a structured or unstructured form, a data scientist becomes essential and can master the art with the right Data Science Training. Hence, there is a need for a data scientist, with sound knowledge of handling data that demands the best data science training and experience.

Software developers collaborate with computer programmers to test the convenience of using a software program. The component that powers digital systems is software which is the core element of computers. To be precise, software engineering can be termed as the application of engineering principles for designing, structuring, developing, and implementing software. An efficient software developer understands the client’s needs and then develops the software as a solution to the client’s needs.

Important tools used in software development and data science

To be productive, data science career aspirants should be knowledgeable about statistics, software programming, and analytical thinking. In addition, a data scientist should have the urge to learn and be updated about new programming languages.  Python, Swift, and Ruby, apart from mastering SQL, Spark, machine learning, and Hadoop are the most important to learn.

A software developer should know the latest programming languages along with a bachelor’s degree in computer science. A good understanding of computer programming by understanding the functionality of new tools is a plus. Along with various skills, a software developer should possess to flourish in the field of analytical skills. Followed by attention to detail, creativity, problem-solving techniques, and interpersonal skills are vital.

The pay scale for data scientists versus software developers

A report from Payscale.com reveals that the median salary for a data scientist is around 6,00,000. It is not much different in the US. According to a survey by Glassdoor, the median salary for a data scientist is $91470, which is great. So, it is evident that data science can offer ample money and growth. But, the salary also depends on the qualifications, skills, experience, and location. Firms also play a vital role in deciding the pay. 

Currently, the median salary for software developers is $1,00,80 and the growth in employing software developers is set to increase by 24% in a decade. In addition, an estimate states that there are 3.7 billion mobile users worldwide. It demands newer and better applications for smartphones and tablets. It increases the demand for software developers globally. Factors that affect the salary of a data scientist are similar to those of software developers. 

Roles and job titles for a career in data science versus software development

A few important job titles that a data scientist can do are data architect, data scientist, and business analyst. In fact, the roles of data administrator, and business intelligence manager are also in demand. Global companies in the digital world, like Facebook, Twitter, IBM, and Apple, are racing to hire the best data scientists. So, the demand for data scientists is growing like never before.

Some of the interesting career options that connect to software developers are Computer programming. Computer system analysts, web developers, computer network architects, and computer hardware engineers are also on the list. Though data science is a relatively newer concept than software development, it is increasing rapidly. 

Conclusion

This article has showcased the digitisation, technology, data rise, and demand of technology professionals. The high demand for data scientists, and software developers have given rise to the skills gaps. If you are eager to learn new tools for efficient data handling, you can grab a chance to flourish as a data scientist in the long run. Enrol for the best data science training course and enter the world of data.
But, selecting the best institute for data science training is not easy.

You should enquire about the faculty, course curriculum, and the job assistance that they provide. It is always good to get interview assistance, career monitoring, and resume-building tips from the experts. If the institute provides all this then it is the best for the learners.

It should be your priority to learn coding and programming if you want to become a software developer. Furthermore, the amount of data being produced every day is high, which signifies long-term growth and attractive salary await a talented data scientist. So, in the end, we can say that both careers in data science and software development are promising. It depends on individual preferences and interests. Both, career options are good and offer career growth. 

For more details, you can also search for – Imarticus Learning.  You can drop your query by filling up a form from the site. You can also contact us through the Live Chat Support system. To explore more you can visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Bangalore, Hyderabad, Delhi, and Gurgaon.

Advanced Data Science Skills to Stay Relevant in the Post-Pandemic World!

Posted on by Imarticus Learning

The need to upskill to meet the dynamic demands of a technology-first world has been around for the past few years; it has only become more urgent in the wake of the COVID19 pandemic. The emergence of new technologies such as Artificial Intelligence, machine learning and data science has set the tone for the future.

Data Science

In the post-COVID19 world, there are a few advanced data science skills that, when added to the toolkit of a data scientist, can make or break their career.

To ensure that your core competencies are strengthened as a data scientist, you can sign up for a comprehensive data science training course that explores the following:

 

#1: Geospatial Technologies

 With more people working on data-driven decision processes, geospatial data has helped better planning and processing of the system. This knowledge has proved invaluable in tracking the COVID19 outbreak all over the world; come the near future, geospatial technology will likely be extended to other research areas as well.

Data Science

A geospatial data scientist will need to sift through vast geographic and demographic datasets that hide gold nuggets of insight across diverse research topics.

 

 

#2: Natural Language Processing (NLP)

NLP gained traction even before the pandemic reared its ugly head. That said, it is only set to increase in importance and reach in a post-pandemic world. Natural Language Processing

Most organisations often implement self-service systems, such as bots that come with multi-language optimized NLP to help solve customer problems.

Data scientists of the future must understand NLP and master it enough to help companies develop automated solutions for a better post-COVID outcome.

#3: Computer Vision

Computer vision is an artificial intelligence field which trains computers to interpret and comprehend the visual world. It uses digital images from cameras and videos as well as deep learning models to recognise and distinguish objects correctly. With the help of algorithms, computer vision is also integrated to follow up with a programmed response. In current scenarios, computer vision has proved helpful in containing the outbreak and regulating quarantines and social distancing in cities across the world. In the future, where maintaining distance might become the norm, data scientists specializing in computer vision will automatically become more hire able.

#4: Data Storytelling

With data analytics becoming a prime concern for companies across industries, the need for good data storytelling has increased. The benefit of data analysis is not just in the evidence it provides but also in how it is made meaningful and impactful. Gripping storytelling makes it easier for non-data-scientist stakeholders to understand the value of the information and the possibilities it poses.

Data presented as contextual stories, rather than isolated data points, makes individuals more likely to understand the impact, decipher patterns and make more informed decisions.

In turn, as data storytelling would help business leaders with powerful insights, it would help them better prepare for the post-pandemic world’s opportunities.

 

#5: Explainable AI

Considering that AI has reached into nearly every area of human life, companies must be able to trust computers and their decisions. This is where the need for explainable AI emerges. Until now, companies build and sourced AI models that predicted accurate insights from large data dumps. In a post-pandemic world, they may well shift to models that also provide explanations for predictions. Explainable AI is a step forward in reducing the mistrust in non-human workflows. It makes AI systems more transparent and much fairer and all-inclusive than they were earlier.

Conclusion

Advanced data science skills are crucial to the cause of innovation and growth. Advanced upskilling is an integral step for data scientists looking to become more than relevant in the coming years.