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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.

          You're reading Data Science And Analytics: The Emerging Opportunities And Trends To Deal With Disruptive Change

          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

          Data Analytics Infrastructure: Current Trends

          Data analytics infrastructure is area that requires constant deep study to remain current.

          The very term data analytics infrastructure is itself far from simple. It’s a wide ranging concept that comprises the many technologies and services that support the essential process of data mining for competitive insight. These many elements include managing, integrating, modeling and – perhaps most important – accessing the rapidly growing data sets that allow companies to better understand their business workflow and forecast market moves.

          The challenge of data analytics is that it changes faster than you can say “business intelligence.” The technology itself is now undergoing rapid evolution, as is the techniques that practitioners are using. This is one sector where even an approach that has seen no refresh in a mere six months is already falling behind.

          To provide a current snapshot, I’ll speak with Brian Wood, Research Director, Dresner Advisory Services. Wood will discuss the new report from Dresner, 2023 Analytical Data Infrastructure Market Study.

          Among the questions we’ll discuss:

          What use case for ADI platforms did most respondents list as a top priority? What does this mean for the ADI market?

          It seem as if corporate standards have been a low priority for ADI, compared with security and performance. What changes do you think this trend will create?

          Is cloud or on-prem more popular for ADI platforms? What about the hybrid platform?

          Are there factors that make creating a coherent strategy for analytics projects difficult? (Like the range of innovation and the variety of ADI platforms.) How can business leaders deal with this challenge?

          Your sense of the future of the ADI market, several years out?

          Listen to the podcast:

          Watch the video:

          Edited highlights from the full discussion – all quotes from Brian Wood:

          “One of the things that tends to help to limit this kind of [problematic] spread across the organization of different components is having a chief data officer, CDO or a chief analytics officer. Because that becomes a focus for them to make sure they have a cohesive and efficient analytic data infrastructure as opposed to a little of this here and a little of that there.

          In most cases [the CDO] don’t really play the role of the cop trying to enforce it, although if you have the C in front of your title, you tend to get attention.”

          “To me, the only difference between governance and compliance is where the requirements come from. Governance is placing requirements on yourself. They’re internally. Compliance is from external.”

          “Corporate standards [for data analytics practices] aren’t important. If one person finds a tool that is purely cloud-based and web-based and it works well for them, they will go ahead and buy it.

          A lot of these tools and products have freemium models where someone can put their personal credit card in and use it for a month and then of course once they get used to the tool they’re not gonna let it go, and it becomes part of your analytic data infrastructure.”

          “One of the things that I find interesting is, even in the large organizations, they want everything in the Cloud, but they’re not starting from a green fields situation. They have lots of On-premise type of systems already.

          But in order to get there from where you are today, you need a hybrid analytic infrastructure.

          It has to report on the On-premise and the Cloud. But of course then you have multicloud as well. You have multiple public clouds, you have virtual private clouds. Having an infrastructure that will work with all of those, and I think particularly for the larger organizations that have been around for longer, it’s a stepping stone on the path if they wanna get to Cloud.

          And most of them do. The survey says that is a preferred deployment approach for most industries and most functions. But in order to get there you have to go through the hybrid to get to a Cloud infrastructure.”

          “So I’m often called an idealist, because I tend to look at the way things should be instead of the way they are. [chuckle]

          So I’ll say, with a grain of salt, I will say that we will have AI capabilities that will enhance the way we do our jobs and not replace them. The future of work part aspect of it is one part of it, but realistically, we have models that do a lot of pieces of what a human brain does well, but there isn’t the master algorithm.

          And so, what you’ll have is you’ll have the ability for an AI system to look at the different analytics in your organization and make recommendations, like, “This is good, but really you’re only using the trending. You’re not using the actual data.”

          Obviously, now we’ve got models that beat the best chess players and all that. But you have to have something to model. So the way humans process information may or may not be the most efficient, but just taking that and putting it on a silicon substrate instead of soft wetware, so to speak – that helps a lot, but you still need the people to say, This is how I think. These are the connections that I make that led me to this conclusion.’”

          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


          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

          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)]


          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.


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

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


          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.


          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.


          How Small Businesses Look To Leverage Big Data And Data Analytics

            Benefits of Big Data for Small Businesses Following are key benefits of big data for small businesses- 1. Quick Access to Information Big data makes the generated information available and accessible at all times for the businesses in real-time. Various tools have been designed for capturing user data and thus, businesses can accumulate the information in terms of customer behavior. This huge chunk of information is readily available for the businesses at their disposal and they can implement effective strategies for improving their prospects. 2. Tracking Outcomes of Decisions Businesses of any size can gain huge amounts of benefits from the data-driven analytics and this calls for the deployment of big data. Big data enable businesses to track the outcomes of their promotional strategies and giving the companies a clear understanding of what works well for them and improves their decisions to gain better results. Small businesses can tap on this information to know which of their brands are being perceived by their key customers. Based on this information, businesses can carry out accurate predictions regarding their techniques and at the same time minimize their risks. 3. Developing Better Products and Services Small businesses can use big data and analytics for determining the current requirements of their prospective customers. Big data can help in analyzing customer behavior based on their previous trends. A proper analysis of customer behavior and its associated data helps businesses to develop better products and services based on their past needs. Big data also determines the performance of certain products and services of the company and how they can be used to meet these demands. Big data now also allows the companies to test their product designs and determine flaws that may cause losses in case that product is marketed. Big data is also used for enhancing after-sales services like- maintenance, support, etc.  4. Cost-Effective Revenues   How Small Businesses Use Data Analytics •  One of the key applications of machine learning for small businesses is by using it for tracking their customers at various stages of the sales cycle. Small businesses have been using data analytics for determining exactly when a given segment of customers are ready to buy and when they’re going to do so. •  Data analytics are also used for improving customer services. Machine learning tools are now able to analyze the conversations taking place between the sales team and customers across various channels. These can provide greater insights into some of the commonly faced issues by the customers and these can be leveraged for ensuring that customers have a great experience with a product/service/brand. •  Data analytics have been providing the SMBs with detailed insights on operational aspects. Data analytics can be of great use when it comes to a detailed analysis of customer behavior. This, in turn, allows the business owners to learn the motivating factors for the consumers to buy products or services. This is of great value as the SMB owners can utilize this information for identifying the market channels to focus in the coming time and thus saving on the marketing spend and thus increasing the market revenue.   Data Analytics Trends in 2023 for Small Businesses 1. Emergence of Deep Learning We have been generating huge volumes of data every day and it is estimated that the humans generate 2.5 quintillions of data. Machines have become more adept and deep learning capabilities are continuing to rise in the coming time. Often considered as a subset of machine learning, deep learning uses an artificial neural network that learns from the huge volume of data. Its working is considered to be similar to that of the human brain. This level of functionality helps the machines to solve high and complex problems with great degrees of precision. Deep learning has been helping small businesses in enhancing their decision-making capabilities and elevating the operations to the next level. Using deep learning, the chatbots are now able to respond with much more intelligence to a number of questions and ultimately creating helpful interactions with the customers. 2.  Mainstreamed Machine Learning 3.  Dark Data Dark data is used for defining those information assets that the enterprises collect, process or store but have failed to utilize. It is that data that holds value but gets eventually lost in the middle. Some common examples of dark data include- unused customer data, email attachments that are opened but left undeleted. It is estimated that dark data is going to constitute 93% of all data in the near future and various organizations look to formulate steps to utilize it.

          What Is The Difference Between Data Science And Machine Learning?

          Introduction  Data Science vs Machine Learning

          AspectData Science Machine Learning DefinitionA multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.A subfield of artificial intelligence (AI) that focuses on developing algorithms and statistical models that allow computer systems to learn and make predictions or decisions without being explicitly programmed.ScopeBroader scope, encompassing various stages of the data lifecycle, including data collection, cleaning, analysis, visualization, and interpretation.Narrower focus on developing algorithms and models that enable machines to learn from data and make predictions or decisions.GoalExtract insights, patterns, and knowledge from data to solve complex problems and make data-driven decisions.Develop models and algorithms that enable machines to learn from data and improve performance on specific tasks automatically.TechniquesIncorporates various techniques and tools, including statistics, data mining, data visualization, machine learning, and deep learning.Primarily focused on the application of machine learning algorithms, including supervised learning, unsupervised learning, reinforcement learning, and deep learning.ApplicationsData science is applied in various domains, such as healthcare, finance, marketing, social sciences, and more.Machine learning finds applications in recommendation systems, natural language processing, computer vision, fraud detection, autonomous vehicles, and many other areas.

          What is Data Science? 

          Source: DevOps School

          What is Machine Learning? 

          Computers can now learn without being explicitly programmed, thanks to the field of study known as machine learning. Machine learning uses algorithms to process data without human intervention and become trained to make predictions. The set of instructions, the data, or the observations are the inputs for machine learning. The use of machine learning is widespread among businesses like Facebook, Google, etc. 

          Data Scientist vs Machine Learning Engineer

          While data scientists focus on extracting insights from data to drive business decisions, machine learning engineers are responsible for developing the algorithms and programs that enable machines to learn and improve autonomously. Understanding the distinctions between these roles is crucial for anyone considering a career in the field.

          Data ScientistMachine Learning EngineerExpertiseSpecializes in transforming raw data into valuable insightsFocuses on developing algorithms and programs for machine learningSkillsProficient in data mining, machine learning, and statisticsProficient in algorithmic codingApplicationsUsed in various sectors such as e-commerce, healthcare, and moreDevelops systems like self-driving cars and personalized newsfeedsFocusAnalyzing data and deriving business insightsEnabling machines to exhibit independent behaviorRoleTransforms data into actionable intelligenceDevelops algorithms for machines to learn and improve

          What are the Similarities Between Data Science and Machine Learning?

          When we talk about Data Science vs Machine Learning, Data Science and Machine Learning are closely related fields with several similarities. Here are some key similarities between Data Science and Machine Learning:

          1. Data-driven approach: Data Science and Machine Learning are centered around using data to gain insights and make informed decisions. They rely on analyzing and interpreting large volumes of data to extract meaningful patterns and knowledge.

          2. Common goal: The ultimate goal of both Data Science and Machine Learning is to derive valuable insights and predictions from data. They aim to solve complex problems, make accurate predictions, and uncover hidden patterns or relationships in data.

          3. Statistical foundation: Both fields rely on statistical techniques and methods to analyze and model data. Probability theory, hypothesis testing, regression analysis, and other statistical tools are commonly used in Data Science and Machine Learning.

          4. Feature engineering: In both Data Science and Machine Learning, feature engineering plays a crucial role. It involves selecting, transforming, and creating relevant features from the raw data to improve the performance and accuracy of models. Data scientists and machine learning practitioners often spend significant time on this step.

          5. Data preprocessing: Data preprocessing is essential in both Data Science and Machine Learning. It involves cleaning and transforming raw data, handling missing values, dealing with outliers, and standardizing or normalizing data. Proper data preprocessing helps to improve the quality and reliability of models.

          Where is Machine Learning Used in Data Science?

          In Data Science vs Machine Learning, the skills required for ML Engineer vs Data Scientist are quite similar. 

          Skills Required to Become Data Scientist

          Exceptional Python, R, SAS, or Scala programming skills

          SQL database coding expertise

          Familiarity with machine learning algorithms

          Knowledge of statistics at a deep level

          Skills in data cleaning, mining, and visualization

          Knowledge of how to use big data tools like Hadoop.

          Skills Needed for the Machine Learning Engineer

          Working knowledge of machine learning algorithms

          Processing natural language

          Python or R programming skills are required

          Understanding of probability and statistics

          Understanding of data interpretation and modeling.

          Source: AltexSoft

          Data Science vs Machine Learning – Career Options

          There are many career options available for Data Science vs Machine Learning.

          Careers in Data Science

          Data scientists: They create better judgments for businesses by using data to comprehend and explain the phenomena surrounding them.

          Data analysts: Data analysts collect, purge, and analyze data sets to assist in resolving business issues.

          Data Architect: Build systems that gather, handle, and transform unstructured data into knowledge for data scientists and business analysts.

          Business intelligence analyst: To build databases and execute solutions to store and manage data, a data architect reviews and analyzes an organization’s data infrastructure.

          Source: ZaranTech

          Careers in Machine Learning

          Machine learning engineer: Engineers specializing in machine learning conduct research, develop, and design the AI that powers machine learning and maintains or enhances AI systems.

          AI engineer: Building the infrastructure for the development and implementation of AI.

          Cloud engineer: Builds and maintains cloud infrastructure as a cloud engineer.

          Computational linguist: Develop and design computers that address how human language functions as a computational linguist.

          Human-centered AI systems designer: Design, create, and implement AI systems that can learn from and adapt to humans to enhance systems and society.

          Source: LinkedIn


          Data Science and Machine Learning are closely related yet distinct fields. While they share common skills and concepts, understanding the nuances between them is vital for individuals pursuing careers in these domains and organizations aiming to leverage their benefits effectively. To delve deeper into the comparison of Data Science vs Machine Learning and enhance your understanding, consider joining Analytics Vidhya’s Blackbelt Plus Program.

          The program offers valuable resources such as weekly mentorship calls, enabling students to engage with experienced mentors who provide guidance on their data science journey. Moreover, participants get the opportunity to work on industry projects under the guidance of experts. The program takes a personalized approach by offering tailored recommendations based on each student’s unique needs and goals. Sign-up today to know more.

          Frequently Asked Questions

          Q1. What is the main difference between Data Science and Machine Learning?

          A. The main difference lies in their scope and focus. Data Science is a broader field that encompasses various techniques for extracting insights from data, including but not limited to Machine Learning. On the other hand, Machine Learning is a specific subset of Data Science that focuses on developing algorithms and models that enable machines to learn from data and make predictions or decisions.

          Q2. Are the skills required for Data Science and Machine Learning the same?

          A. While there is some overlap in the skills required, there are also distinct differences. Data Scientists need strong statistical knowledge, programming skills, data manipulation skills, and domain expertise. In addition to these skills, Machine Learning Engineers require expertise in implementing and optimizing machine learning algorithms and models.

          Q3. What is the role of a Data Scientist?

          A. The role of a Data Scientist involves collecting and analyzing data, extracting insights, building statistical models, developing data-driven strategies, and communicating findings to stakeholders. They use various tools and techniques, including Machine Learning, to uncover patterns and make data-driven decisions.

          Q4. What is the role of a Machine Learning Engineer?

          A. Machine Learning Engineers focus on developing and implementing machine learning algorithms and models. They work on tasks such as data preprocessing, feature engineering, model selection, training and tuning models, and deploying them in production systems. They collaborate with Data Scientists and Software Engineers to integrate machine learning solutions into applications.


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