Trending February 2024 # 5 Ways Companies Deal With The Data Science Talent Shortage # Suggested March 2024 # Top 6 Popular

You are reading the article 5 Ways Companies Deal With The Data Science Talent Shortage updated in February 2024 on the website Daihoichemgio.com. We hope that the information we have shared is helpful to you. If you find the content interesting and meaningful, please share it with your friends and continue to follow and support us for the latest updates. Suggested March 2024 5 Ways Companies Deal With The Data Science Talent Shortage

Specialized fields like data science have been hit especially hard with recruitment and retention challenges amid the shortage of talent in the tech industry. 

Tech leaders say companies need to reconsider how they source and retain data science talent.

Read on to learn how different companies are combating the data science talent shortage through improved hiring practices, increased retention focus, and a heavier emphasis on efficient tools and teams:

Also read: Today’s Data Science Job Market

When a company is struggling to find new talent for their data science teams, it’s often worth the time and resources to look internally first. 

Current employees are likely to already have some of the skill sets that the company needs, and they already know how the business works. Many companies are upskilling these employees who want to learn and find a new role within the company or expand their data science responsibilities.

Waleed Kadous, head of engineering at Anyscale, an artificial intelligence (AI) application scaling and development company, believes that employees with the right baseline skills can be trained as data scientists, particularly for more straightforward data science tasks.

“It depends on the complexity of the tasks being undertaken, but in some cases, internal training of candidates who have a CS or statistics background is working well,” Kadous said. “This doesn’t work well for highly complex data science problems, but we are still at a stage of having low-hanging fruit in many areas. 

“This often works well with the central bureau model of data science teams, where data scientists embed within a team to complete a project and then move on. … The central bureau incubates pockets of data science talent through the company.”

Continue your data science education: 10 Top Data Science Certifications

In many cases, data science teams already have all of the staffing they need, but inefficient processes and support hold them back from meaningful projects and progress. 

Marshall Choy, SVP of product at SambaNova Systems, an AI innovation and dataflow-as-a-service company, believes many tasks that are handled by internal data scientists can be better administered by third-party strategic vendors and their specialized platforms.

“Some companies are taking a very different approach to the talent shortage issue,” Choy said. “These organizations are not acquiring more talent and instead are making strategic investments into technology adoption to achieve their goals.

“By shifting from a DIY approach with AI adoption to working with strategic vendors that provide higher-level solutions, these companies are both reducing cost and augmenting their data science talent. 

“As an example, SambaNova Systems’ dataflow-as-a-service eliminates the need for large data science teams, as the solution is delivered to companies as a subscription service that includes the expertise required to deploy and maintain it.”

Dan DeMers, CEO and co-founder of Cinchy, a dataware company, also believes that third-party solutions can solve data science team pain points and reduce the need for additional staff. Great tools also have the potential to draw in talent who want access to these types of resources.

“Data is seen as inextricably intertwined with the applications used to generate, collate, and analyze it, and along the way, some of those functions have become commoditized. That’s partly why data science has gone from being the discipline du jour to a routine task.

Kon Leong, CEO at ZL Technologies, an enterprise unstructured data management platform, thinks that one of the biggest inefficiencies on data science teams today is asking specialized data scientists to focus on menial tasks like data cleaning.

“In many ways, the data cleanup and management challenge has eclipsed the analysis portion. This creates a mismatch where many professionals end up using their skills on tedious work that they’re overqualified for, even while there is still a shortage of top talent for the most difficult and pressing business problems.

“Some companies have conceived creative ways to tackle data cleanup, such as through cutting-edge data management and analytics technologies that enable non-technical business stakeholders to leverage insights. This frees up a company’s data scientists to focus on the toughest challenges, which only they are trained to do. The result is a better use of existing resources.”

Improve data quality with the right tools: Best Data Quality Tools & Software

These newer data professionals are hungry to showcase their learned skills, but they also want opportunities to keep learning, try hands-on tasks, and build their network for professional growth.

Sean O’Brien, senior VP of education at SAS, a top analytics and data management company, thinks it’s important for retention for companies to offer curated networking opportunities, where new data scientists can build their network and peer community within an organization.

“Without as much face time, new and early career employees have lost many of the networking and relationship-building opportunities that previously created awareness of hidden talent,” O’Brien said.  

“Long-serving team members already have established relationships and knowledge of the work processes. New employees lack this accumulated workplace social capital and report high dissatisfaction with remote work. 

“Companies can set themselves apart by creating opportunities for new employees to generate connections, such as meetings with key executives, leading small projects, and peer-to-peer communities.”

O’Brien also emphasized the importance of having a strong university recruiting and education strategy, so companies can engage data science talent as early as possible.

“Creating an attractive workplace for analytics talent isn’t enough, however,” O’Brien said. “Companies need to go to the source for talent by working directly with local universities.

“Many SAS customers partner with local college analytics and data science programs to provide data, guest speakers, and other resources, and establish internship and mentor programs that lead directly to employment.

“By providing real-world data for capstone and other student projects, graduates emerge with experience and familiarity with a company’s data and business challenges. SAS has partnerships with more than 400 universities to help connect our customers with new talent.”

The importance of data to your business: Data-Driven Decision Making: Top 9 Best Practices

Data science professionals at all levels want transparency, not only on salary and work expectations but also on what career growth and paths forward could look like for them.

Jessica Reeves, SVP of operations at Anaconda, an open-source data science platform, explained the importance of being transparent with job candidates and current employees across salary, communication, and career growth opportunities.

“Transparency is a critical characteristic that allows Anaconda to attract and retain the best talent,” Reeves said. 

“This is seen through salary transparency for each employee with benchmarks in the industry for your title, where you live, and how your salary stacks comparative to other jobs with the same title. We also encourage transparency by having an open-door policy, senior leadership office hours, and anonymous monthly Ask Me Anything sessions with senior leadership. 

“Prioritizing career growth also helps attract top talent. Now more than ever, employees want a position where they can have opportunities to get to the next level and know what that path is. Being a company that makes its potential trajectory clear from the start allows us to draw in the best data practitioners worldwide. 

“To showcase their growth potential at Anaconda, we have clear career mapping tracks for individual contributors and managers, allowing each person to see the steps necessary to reach their goal.”

Read next: Data Analytics Industry Review

Developing and projecting a recognizable brand voice is one of the most effective indirect recruiting tactics in data science. 

If a job seeker has heard good things about your company or considers you a top expert in data science, they are more likely to find and apply for your open positions.

“One thing that is becoming increasingly important is supporting data scientists in sharing their work through blog posts and conferences,” Kadous said. “Uber’s blog is a great example of that.

“It’s a bit tricky because sometimes data science is the secret sauce, but it’s also important as a recruiting tool: It demonstrates the cool work being done in a particular place.

Reeves at Anaconda also encourages her teams to find different forums and mediums to give their brand more visibility.

“Our Anaconda engineering team is very active in community forums and events,” Reeves said. “We strive to ingrain ourselves into the extensive data and engineering community by engaging on Twitter, having guest appearances on webinars and podcasts, or authoring blog posts on data science and open-source topics.”

Read next: Top 50 Companies Hiring for Data Science Roles

You're reading 5 Ways Companies Deal With The Data Science Talent Shortage

Data Science And Analytics: The Emerging Opportunities And Trends To Deal With Disruptive Change

blog / General Data Science and Analytics: The Emerging Opportunities and Trends To Deal With Disruptive Change

Share link

Top 5 data science trends that are revolutionizing business operations in a rapidly changing economy and opening up new career prospects.

What do Amazon, BuzzFeed, and Spotify have in common? They’re all three successful, data-driven, and data reliant. “Customers also liked” to “Which Harry Potter character are you?” to “Discover Weekly”, all of these are a result of robust data science technology and data scientists. Globally, industries have first-hand seen what leveraging data science technology can do for their businesses. Data-driven decision-making enables organizations to respond to consumer trends, offers businesses growth opportunities, and equips them to predict and tackle challenges in a disruptive economy. 

Almost every business today receives large volumes of data that seem overwhelming and chaotic. This is the very same data that builds rich customer experiences, simplifies business decisions, and creates innovations that enrich lives across industries. However, in isolation, data is just that – a bunch of rows and columns with hidden insights.  

In the light of data challenges facing enterprises, we’ve summarized a few data science trends as well as prospects for data scientists. 

Enterprises choose data science as a core business function 

Several companies and their leaders are identifying the value of big data. Businesses are investing heavily in AI and ML technologies to capture more data and capitalize on it. Organizations are investing in data scientists as well to harness those crucial insights for their businesses.

 “76% percent of businesses plan on increasing investment in analytics capabilities over the next two years”

However, around 60% of data within an enterprise goes unused for analytics. Unlocking the power of big data is pushing organizations to shift data analytics to a core function led by Chief Data Officers (CDO). CDOs are expected to work closely with CEOs on holistic data strategies to deliver insights that help navigate disruptions.

Data Scientists and Chief Data Officers are in demand across industries

The average growth rate for all occupations is 8%, whereas data scientist roles are expected to grow by 27% by 2030.

A quick glimpse through Glassdoor shows that a data scientist job ranks second in the list of 50 Best Jobs in America for 2023, with an average base salary of $113,736 per year.

Employers need skilled data scientists, not just data analysts 

Navigating big data requires a curious mind, a passion for analyzing data patterns, and the ability to predict and derive actionable insights. Businesses today require data science professionals who are technical specialists and can communicate business strategy across functions in an enterprise. While there are learning institutions that offer degrees in data science and analytics, professionals need to be agile to changing business environments. Data Scientists will need to engage in lifelong learning to keep up with the digital transformation, the complexity, and volumes of data that continue to emerge. Data science professionals that upskill and reskill their abilities through their career will find an accelerated path to senior roles in organizations. Emeritus offers mid-level and senior-level professionals high-quality online programs from reputed global universities that enable them to compete in this data-driven economy.

View all data science and data analytics courses. 

CDOs will spearhead a data-driven culture across the enterprise.

Enhanced Customer Experiences via data-driven technologies 

Practically every industry today benefits from data science and analytics. While some large businesses leverage the power of data at a macro level to support bottom-line growth, data analytics also equips other businesses with actionable strategies to tackle future challenges in a data-driven economy.

To learn Data Science and Analytics, visit our program page.

5 Ways To Enable Business Agility With Data Monitoring

Data is an important guide for businesses to make data-driven decisions and get a realistic view of the industry they operate in. Data consistency is critical for analyzing and observing the status of businesses. That’s where data monitoring comes in. It offers businesses the opportunity to continuously check the quality of their data.

What is data monitoring?

Data monitoring refers to the process of measuring and evaluating data to avoid degradation in quality over time. Data monitoring, for example, assesses the quality of your data to ensure that it meets or fulfills pre-determined business purposes.

What are the benefits of data monitoring?

Data monitoring enables business agility by checking the quality of data at the time of creation. Here are some key benefits of data monitoring for businesses: 

Solve issues faster: Data monitoring ensures that data problems are identified as soon as they occur and enables businesses to intervene and resolve them.

More accurate data-driven decision-making: Maintaining the quality of data enables businesses to make accurate decisions based on data.

In addition, data monitoring is essential for tracking the performance of machine learning models. In order to get optimal outcomes from ML models over time, companies should monitor data used to train ML models. It allows businesses to analyze the accuracy of model prediction and detect data issues early.

1. Detect Existing Problems

Data monitoring helps with detecting existing problems in data. Some examples of how data quality issues arise include:

Data duplication: It commonly occurs because of human error. If not detected, it can cause skewed metrics.

Missing data: These are missing values in databases. Missing values can occur due to a lack of observation or due to human error.

Ambiguous data: It means that two separate data points in a database cannot be distinguished from each other. These types of data are vague and open to multiple interpretations.

Data drift: Changes in data over time due to changing environments.

Data mismatches: It is also called ‘data match error’, and causes inconsistencies in the data. This is a situation where data types don’t match input values.

Recommendation: Leverage data monitoring to ensure that data problems are identified as soon as they occur. This enables you to intervene and resolve them faster.

2. Predict  potential issues

Not only detecting existing problems but also predicting potential problems, such as data security and optimization issues, is a factor that improves the data monitoring process. Predicting potential problems saves businesses resources and time by allowing them to be more flexible and plan for the long term.

Recommendation: Enable more accurate data-driven decision-making by using data monitoring. Maintaining the quality of data enables businesses to make accurate decisions based on data.

3. Track the performance of machine learning models

Data monitoring is essential for tracking the performance of machine learning models. In order to get optimal outcomes from ML models over time, companies should monitor data used to train ML models. It allows businesses to analyze the accuracy of model prediction and detect data issues early.

4. Utilize alerts and dashboards

Monitoring is a process and good planning of the process can improve it. For instance,

You can use dashboards that provide a summarized view of the system in the form of charts and graphs and contain historical data.

You can set up alerts responding to the changes to your data and notify you when a problem occurs.

Recommendation: 

Use dashboards that provide a summarized view of the system in the form of charts and graphs and contain historical data.

Set up alerts responding to the changes to your data and notify you when a problem occurs.

It is important that you evaluate each situation you monitor in its own context. By interpreting the information you receive through the data monitoring process in its own way, you can better identify problems. At this stage, you can get more accurate and consistent results by choosing the right tool for your business.

You can also check our article on model monitoring.

If you have questions about which solutions to choose, we are happy to help:

Gulbahar Karatas

Gülbahar is an AIMultiple industry analyst focused on web data collections and applications of web data.

YOUR EMAIL ADDRESS WILL NOT BE PUBLISHED. REQUIRED FIELDS ARE MARKED

*

0 Comments

Comment

How To Deal With Missing Data Using Python

This article was published as a part of the Data Science Blogathon

Overview of Missing Data

Real-world data is messy and usually holds a lot of missing values. Missing data can skew anything for data scientists and, A data scientist doesn’t want to design biased estimates that point to invalid results. Behind, any analysis is only as great as the data. Missing data appear when no value is available in one or more variables of an individual. Due to Missing data, the statistical power of the analysis can reduce, which can impact the validity of the results.

This article will help you to a guild the following topics.

The reason behind missing data?

What are the types of missing data?

Missing Completely at Random (MCAR)

Missing at Random (MAR)

Missing Not at Random (MNAR)

Detecting Missing values

Detecting missing values numerically

Detecting missing data visually using Missingno library

Finding relationship among missing data

Using matrix plot

Using a Heatmap

Treating Missing values

Deletions

Pairwise Deletion

Listwise Deletion/ Dropping rows

Dropping complete columns

Basic Imputation Techniques

Imputation with a constant value

Imputation using the statistics (mean, median, mode)

K-Nearest Neighbor Imputation

let’s start…..

What are the reasons behind missing data?

Missing data can occur due to many reasons. The data is collected from various sources and, while mining the data, there is a chance to lose the data. However, most of the time cause for missing data is item nonresponse, which means people are not willing(Due to a lack of knowledge about the question ) to answer the questions in a survey, and some people unwillingness to react to sensitive questions like age, salary, gender.

Types of Missing data

Before dealing with the missing values, it is necessary to understand the category of missing values. There are 3 major categories of missing values.

Missing Completely at Random(MCAR):

A variable is missing completely at random (MCAR)if the missing values on a given variable (Y) don’t have a relationship with other variables in a given data set or with the variable (Y) itself. In other words, When data is MCAR, there is no relationship between the data missing and any values, and there is no particular reason for the missing values.

Missing at Random(MAR):

Let’s understands the following examples:

Women are less likely to talk about age and weight than men.

Men are less likely to talk about salary and emotions than women.

familiar right?… This sort of missing content indicates missing at random.

MAR occurs when the missingness is not random, but there is a systematic relationship between missing values and other observed data but not the missing data.

Let me explain to you: you are working on a dataset of ABC survey. You will find out that many emotion observations are null. You decide to dig deeper and found most of the emotion observations are null that belongs to men’s observation.

Missing Not at Random(MNAR):

The final and most difficult situation of missingness. MNAR occurs when the missingness is not random, and there is a systematic relationship between missing value, observed value, and missing itself. To make sure, If the missingness is in 2 or more variables holding the same pattern, you can sort the data with one variable and visualize it.

Source: Medium

‘Housing’ and ‘Loan’ variables referred to the same missingness pattern.

Detecting missing data

Detecting missing values numerically:

First, detect the percentage of missing values in every column of the dataset will give an idea about the distribution of missing values.

import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns import warnings # Ignores any warning warnings.filterwarnings("ignore") train = pd.read_csv("Train.csv") mis_val =train.isna().sum() mis_val_per = train.isna().sum()/len(train)*100 mis_val_table = pd.concat([mis_val, mis_val_per], axis=1) mis_val_table_ren_columns = mis_val_table.rename( columns = {0 : 'Missing Values', 1 : '% of Total Values'}) mis_val_table_ren_columns = mis_val_table_ren_columns[ mis_val_table_ren_columns.iloc[:,:] != 0].sort_values( '% of Total Values', ascending=False).round(1) mis_val_table_ren_columns

Detecting missing values visually using Missingno library :

Missingno is a simple Python library that presents a series of visualizations to recognize the behavior and distribution of missing data inside a pandas data frame. It can be in the form of a barplot, matrix plot, heatmap, or a dendrogram.

To use this library, we require to install  and import it

pip install missingno import missingno as msno msno.bar(train)

The above bar chart gives a quick graphical summary of the completeness of the dataset. We can observe that Item_Weight, Outlet_Size columns have missing values. But it makes sense if it could find out the location of the missing data.

The msno.matrix() is a nullity matrix that will help to visualize the location of the null observations.

The plot appears white wherever there are missing values.

Once you get the location of the missing data, you can easily find out the type of missing data.

Let’s check out the kind of missing data……

Both the Item_Weight and the Outlet_Size columns have a lot of missing values. The missingno package additionally lets us sort the chart by a selective column. Let’s sort the value by Item_Weight column to detect if there is a pattern in the missing values.

sorted = train.sort_values('Item_Weight') msno.matrix(sorted)

The above chart shows the relationship between Item_Weight and Outlet_Size.

Let’s examine is any relationship with observed data.

data = train.loc[(train["Outlet_Establishment_Year"] == 1985)]

data

The above chart shows that all the Item_Weight are null that belongs to the 1985 establishment year.

The Item_Weight is null that belongs to Tier3 and Tier1, which have outlet_size medium, low, and contain low and regular fat. This missingness is a kind of Missing at Random case(MAR) as all the missing Item_Weight relates to one specific year.

msno. heatmap() helps to visualize the correlation between missing features.

msno.heatmap(train)

Item_Weight has a negative(-0.3) correlation with Outlet_Size.

After classified the patterns in missing values, it needs to treat them.

Deletion:

The Deletion technique deletes the missing values from a dataset. followings are the types of missing data.

Listwise deletion:

Listwise deletion is preferred when there is a Missing Completely at Random case. In Listwise deletion entire rows(which hold the missing values) are deleted. It is also known as complete-case analysis as it removes all data that have one or more missing values.

In python we use dropna() function for Listwise deletion.

train_1 = train.copy() train_1.dropna()

Listwise deletion is not preferred if the size of the dataset is small as it removes entire rows if we eliminate rows with missing data then the dataset becomes very short and the machine learning model will not give good outcomes on a small dataset.

Pairwise Deletion:

Pairwise Deletion is used if missingness is missing completely at random i.e MCAR.

Pairwise deletion is preferred to reduce the loss that happens in Listwise deletion. It is also called an available-case analysis as it removes only null observation, not the entire row.

All methods in pandas like mean, sum, etc. intrinsically skip missing values.

train_2 = train.copy() train_2['Item_Weight'].mean() #pandas skips the missing values and calculates mean of the remaining values.

Dropping complete columns

If a column holds a lot of missing values, say more than 80%, and the feature is not meaningful, that time we can drop the entire column.

Imputation techniques:

The imputation technique replaces missing values with substituted values. The missing values can be imputed in many ways depending upon the nature of the data and its problem. Imputation techniques can be broadly they can be classified as follows:

Imputation with constant value:

As the title hints — it replaces the missing values with either zero or any constant value.

 We will use the SimpleImputer class from sklearn.

from sklearn.impute import SimpleImputer train_constant = train.copy() #setting strategy to 'constant' mean_imputer = SimpleImputer(strategy='constant') # imputing using constant value train_constant.iloc[:,:] = mean_imputer.fit_transform(train_constant) train_constant.isnull().sum()

Imputation using Statistics:

The syntax is the same as imputation with constant only the SimpleImputer strategy will change. It can be “Mean” or “Median” or “Most_Frequent”.

“Mean” will replace missing values using the mean in each column. It is preferred if data is numeric and not skewed.

“Median” will replace missing values using the median in each column. It is preferred if data is numeric and skewed.

“Most_frequent” will replace missing values using the most_frequent in each column. It is preferred if data is a string(object) or numeric.

Before using any strategy, the foremost step is to check the type of data and distribution of features(if numeric).

train['Item_Weight'].dtype sns.distplot(train['Item_Weight'])

Item_Weight column satisfying both conditions numeric type and doesn’t have skewed(follow Gaussian distribution). here, we can use any strategy.

from sklearn.impute import SimpleImputer train_most_frequent = train.copy() #setting strategy to 'mean' to impute by the mean mean_imputer = SimpleImputer(strategy='most_frequent')# strategy can also be mean or median train_most_frequent.iloc[:,:] = mean_imputer.fit_transform(train_most_frequent) train_most_frequent.isnull().sum()

Advanced Imputation Technique:

Unlike the previous techniques, Advanced imputation techniques adopt machine learning algorithms to impute the missing values in a dataset. Followings are the machine learning algorithms that help to impute missing values.

K_Nearest Neighbor Imputation:

The KNN algorithm helps to impute missing data by finding the closest neighbors using the Euclidean distance metric to the observation with missing data and imputing them based on the non-missing values in the neighbors.

train_knn = train.copy(deep=True) from sklearn.impute import KNNImputer knn_imputer = KNNImputer(n_neighbors=2, weights="uniform") train_knn['Item_Weight'] = knn_imputer.fit_transform(train_knn[['Item_Weight']]) train_knn['Item_Weight'].isnull().sum()

The fundamental weakness of KNN doesn’t work on categorical features. We need to convert them into numeric using any encoding method. It requires normalizing data as KNN Imputer is a distance-based imputation method and different scales of data generate biased replacements for the missing values.

Conclusion

There is no single method to handle missing values. Before applying any methods, it is necessary to understand the type of missing values, then check the datatype and skewness of the missing column, and then decide which method is best for a particular problem.

The media shown in this article are not owned by Analytics Vidhya and are used at the Author’s discretion.

Related

Top 10 Data Science Companies In India To Work For 2023

A rundown of top data science companies for 2023

Data Science is an umbrella term that covers areas – Data Analytics, Big Data, Business Analytics, Machine Learning, Artificial Intelligence and Deep Learning. This immense field has changed what businesses look like into data and convert them into usable insights. Advancement in technologies and data science tools have changed the manners by which organizations work and grow. India, being a mother lode of ability, is the top destination for national and global companies searching for qualified Data Science experts. Over recent years, the demand for Data Scientists has developed exponentially. More than 97,000 data science jobs are open in 2023 just in India. Let’s look at some of the top data science companies that you can consider to land your next job.  

1.  Ugam, a Merkle company

Ugam, a Merkle company and part of Dentsu, is a leading next-generation data and analytics company helping businesses make superior decisions. Ugam’s customer-centric approach blends data, technology, and expertise, enabling impactful and long-tenured relationships with more than 85 Fortune 500 companies. Recognized as one of the best firms for data scientists to work, Ugam’s data scientists get an opportunity to work directly with client business stakeholders on end-to-end projects. Leveraging Ugam’s proprietary analytical tools, frameworks, and AI / ML technology (Ugam’s JARVIS), they deliver superior results across industries like Retail, Hi-Tech, BFSI, Distribution and Manufacturing as well as Market Research and Consulting. Ugam not only offers data scientists an opportunity to accelerate their career but also offers stability and a unique culture. Led by its founding partners and a committed leadership team, it has experienced significant Y-O-Y growth since inception. The company has a high percentage of long tenured (more than 10 years) and boomerang data scientists. Ugamites vouch for Ugam’s people-centric culture, upskilling, and mobility (across geographies or teams) opportunities and positively driven work environment, thereby making it Analytics Insight’s top pick for data scientists to work.  

2.  MuSigma

Recognizing itself as the biggest solution provider of decision science and analytics, MuSigma has its headquarters in Chicago, US. It has workplaces world-over, with Bangalore as its central delivery hub. Your role as a Data Scientist at MuSigma would include analysing data, refining as well as rearranging it and lastly assessing the outcomes. Mu Sigma is one of the favorite places for employees to work in the field of data science because of its open culture. It’s quite well-known as it serves Fortune 500 organizations through decision science and big data analytics. Mu Sigma has a creative and interactive way to invite its new employees through what they call MSU (Mu Sigma University), where the new employees get hands-on training on various challenging projects under the direction of senior experts in the organization.  

3.  Manthan

Another leading data science firm is Manthan! They have an extraordinary methodology with regards to business solutions. They collaborate the power of AI and analytics that gives data-driven insights of a business model. They assist organizations with making educated decisions by means of rigid data analysis and technology. This organization serves various businesses from technology to telecom, just as retail, pharma, and travel. Its data scientists give an analytics model for the decision-making of its customers. At Manthan, data scientists are constantly encouraged to test performance of different data-driven products using leading technologies like AI, ML, etc. that are relative to their domain. They are given the opportunity to manage large amounts of data to find valuable insights that streamline business procedures, identify opportunities using research and management tools, and reduce risks.  

4.  Absolutdata

Absolutdata bestows impressive learning and growth curves than different players in the market. With explicit, role-based learning on niche subjects, data scientists can upskill and concentrate on fortifying core fundamentals. Moreover, they are urged to take up new jobs and responsibilities that draw out the best in them while permitting them to grow into influential positions.  

5.  Fractal Analytics

Founded in 2000, Fractal Analytics has developed as one of the top analytics service providers in the nation. With a worldwide impression bragging about a few Fortune 500 organizations from ventures like retail, insurance and technology, there is unquestionably no halting this one. The organization is at present employing Data Scientists for its workplaces in Bangalore, Mumbai and Gurgaon. Fractal Analytics has built-up a good customer base. Hence, as a data scientist you’ll be working on significant projects such as business analytics, healthcare, and decision-making. With the pool of data scientists working at Fractal increasing, they’ve started providing training and mentorship programs that will enable its employees to enhance their skills. You’ll primarily work on forecasting projects. If data analytics and forecasting is what you wish to do, Fractal Analytics is your place then.  

6.  BRIDGEi2i Analytics

Established in India in May 2011 by Prithvijit Roy (CEO), Pritam K Paul (CTO) and Ashish Sharma (COO) , their asset-based consulting approach covers the entire range, directly from data science to machine learning-centered knowledge improvement to actionability by way of AI accelerators implementation and finally contextualization of the goal to the companies. The company has an attrition rate of 10-12% and believes in sustaining its talent pool with constant, experiential-based learning. It offers its employees chances to progress rapidly into leadership roles. The company has a strong recognition system that guarantees that all the contributions of employees are valued and appropriately awarded.  

7.  Latent View

Latent View furnishes customers with a range of data science services like counseling, Data Architecture and Design, and data implementation and operations. They are upheld by scalable modern architecture. The work culture is friendly and development-oriented. They encourage workers to see each viewpoint in three edges: team, customer, and society. Having Paypal, IBM, Microsoft, and Cisco as the organization’s esteemed customers, it urges data scientists to have a 360-perspective on each project to permit customers to streamline decisions on investment and anticipate the most recent revenue streams as well as to predict product trends. The most compelling motivation they provide and hold individuals is a mix of learning and working with an excellent peer group in addition to a chance to take care of complex business issues with acing analytics abilities in a climate that makes the whole journey rewarding.  

8.  Accenture

Accenture emphasizes that enormous and complex organizations can profit by efficient utilization of their own data. They have to depend on the experts for it – data scientists. If you are passionate about characterizing procedures and delivering on them with the assistance of creative utilization of integrated data then Accenture is the company to be. The unmistakable worldwide professional service provider has openings for data scientists in the field of business process specialization and data management to give some examples. At Accenture, data scientists will also be exposed on the strategy side as well. They’ll be responsible to define strategies and provide solutions using vast amounts of data.  

9.  Genpact

Genpact has more than 1500 data scientists who work as a centralised hub model with customer experience as its main concern. The organization centers around developing the pool of citizen data scientists through different projects, for example, Machine Learning Incubator’ and ‘ML Upgrade’. ML Incubator program is an in-house AI/ML college which aims to upskill more than 600 existing domain experts every year by giving them structured and instructor paced learning structure, in the fields of data engineering, data science.  

10. Tiger Analytics

Tiger Analytics is spearheading what AI and analytics can do to take care of the absolute hardest issues encountered by companies worldwide. They create bespoke solutions fueled by data and technology for a few Fortune 500 organizations. They have workplaces in various urban communities across the US, India, and Singapore, and a substantial remote global workforce.

Top 5 Data Science & Machine Learning Repositories On Github In Feb 2023

Introduction

Continuing our theme of collecting and sharing the top machine learning GitHub repositories every month, the February edition is fresh off the shelves ready for you!

GitHub repositories are one of the easiest and best things for all the people working in data science to keep ourselves updated with the latest developments and projects. It’s also an awesome collaboration tool where we can connect with other like minded data scientists on various projects.

Without any further ado, let’s dive into this month’s list.

This is part of a series from Analytics Vidhya that will run every month. You can check out the top 5 repositories that we picked out in January here.

FastPhotoStyle is a python library developed by NVIDIA. The model takes a content photo and a style photo as inputs. It then transfers the style of the style photo to the content photo.

The developers have cited two examples to show how the algorithm works. The first is a very simple iteration – you download a content and a style image, re-size them, and then simply run the photorealistic image stylization code. In the second example, semantic label maps are used to create the stylized image.

You can read more about this library on Analytics Vidhya’s blog here.

If you’ve ever scraped tweets from Twitter, you have experience working with it’s API. It has it’s limitations and is not easy to work with. This python library was created with that in mind – it has no API rate limits (does not require authentication), no limitations, and is ultra quick. You can use this library to scrape the tweets of any user trivially

The developer has mentioned that it can be used for making Markov Chains. Do note that it works only with python version 3.6+.

This is an implementation of the handwriting synthesis experiments presented in the ‘Generating Sequences with Recurrent Neural Networks’ paper by Alex Graves. As the name of the repository suggests, you can generate different styles of handwriting. The model is based on priming and biasing. Priming controls the style of the samples and biasing controls the neatness of the samples.

The samples presented by the author on the GitHub page are truly fascinating in their diversity. He is looking for contributors to enhance the repository so if you’re interested, get in touch with him!

This is a PyTorch implementation of “Efficient Neural Architecture Search (ENAS) via Parameters Sharing”. What do ENAS do? They reduce the computational requirement, that is, the GPU Hours of the Neural Architecture Search by an incredible 1000 times. They do this via parameter sharing between models that are subgraphs within a large computational graph.

The process of how to use it have been neatly explained on the GitHub page. The prerequisites for implementing this library are:

Python 3.6+

PyTorch

tqdm, imageio, graphviz, tqdm, tensorboardX

This is a relatively straightforward, yet utterly fascinating, use of machine learning. Using a convolutional neural network in python, the developer has built a model that can recognize the hand gestures and convert it into text on the machine.

The author of this repository built the CNN model using both TensorFlow and Keras. He has specified, in detail, how he went about creating this project and each step he followed. It’s definitely worth checking out and trying once on your own machine.

Related

Update the detailed information about 5 Ways Companies Deal With The Data Science Talent Shortage on the Daihoichemgio.com website. We hope the article's content will meet your needs, and we will regularly update the information to provide you with the fastest and most accurate information. Have a great day!