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The Impact of Data Science on Various Industries

Posted on by Imarticus Learning

Data Science and Industrial Integration

Data science studies the extraction and derivation of meaningful insights from data. The approach to gathering the insight is multidisciplinary.

It takes practices and principles from a variety of fields. Some of these fields are computer engineering, statistics, artificial intelligence, and mathematics.

The applications of data science are unlimited as it answers the four important questions of problem-solving. These are what is the situation, why is it, what is the future course, and what would the result yield.

best big data analytics course

This basic information gathering and problem-solving make the adoption of data science in different industries feasible.

Data science finds its application in industries such as retail, medicine, banking, finance, and telecommunications among others. Read ahead if you are planning to make a career in data science and are curious about the impact on various industries.

Impact of Data Science on various industries

A career in data science requires professionals to make use of raw data for the identification of patterns. These patterns are then used to derive actionable insights from the data.

With data science, it is possible to predict future outcomes with a higher level of accuracy. Here is how various industries are benefiting from the applications of data science.

1. Healthcare

Applications of data science in the healthcare industry include the use of data in taking important decisions and drawing conclusions. These include the implementation of medical knowledge gathered in the diagnosis of diseases.

Data science is also used to improve the safety and quality of healthcare by helping design prevention plans. It has also helped chart important health parameters such as sugar level, stress level, brain activity, etc using trackers. This can help deliver personalised care and precise prescriptions.  A deep learning technique is used in reading imaging data. This is also to reduce diagnostic failures.

2. Retail

In the retail industry applications of data science have led to the implementation of features such as recommendation engines. The impact helps companies design products according to the needs of the consumer. Market-based analysis allows the company to determine the likes, dislikes, and price points of consumers. This allows companies to target the right audience more efficiently.

The science has also helped the industry to analyze customer sentiments more accurately. Social media and other points of contact between consumers and companies help companies collect data. This is further used to enhance the feedback mechanism. The feedback is then used to design better products. Firms using data science can optimise prices efficiently. All these lead to an increase in sales and revenue flow in the industry.

3. Banking, Finance Services, and Insurance Industry (BFSI)

BFSI has benefitted largely from the application of data science in the industry. The industry has been able to minimize its losses with the help of fraud detection using science. It has impacted more efficient management of customer data, risk modelling, and customer support.

Data scientists can generate customer life value prediction that is used to recommend products and services to consumers as per their needs. BFSI companies are also able to minimize the impact of any risk to their businesses with the help of predictive and real-time analysis. These techniques have led firms to increase sales and operational gains.

4. Telecommunication Industry

If you are looking for a career in data science in the telecommunication industry you will be able to impact efficient data transmission and visualisation needs. Data science is applied for increasing fraud detection by firms. Using science, consumers benefit from increased network security.

The benefits for the company include price optimisation, real-time analysis, and prevention of customer churn. Companies can also benefit from features such as location-based promotion, predictive analysis, and targeted marketing.

5. The course you need for a career in data science

The example of the impact of data science on four industries is a mirror of the significance of data science in today’s world. If you are looking for a career in data science Imarticus Learning’s Postgraduate Program In Data Science And Analytics is the one.

The course is designed for early career professionals. This six-month program is designed based on a job-specific curriculum. The learning is accentuated by live learning modules, real-world projects, and KPMG hackathons. The certification is designed to launch your career in data science in an industry of your choice.

Summing it up

Applications of data science are wide. There are several industries apart from healthcare, retail, BFSI, and telecom that are using data science. Data science can help companies grow and the skills of a data scientist are always in demand. You can benefit from a quick start in your career by being a part of Learning’s Postgraduate Program In Data Science And Analytics.

Imaritcus Learning offers you benefits such as Job-assurance, specialized training, and placement drive. It has helped more than 56000 individuals attain placements. Be sure to visit us for other similar courses.

11 Innovative Ways to Improve Data Visualisation

Posted on by Imarticus Learning

Organisations gather a huge volume of data each day. This data, in its raw form, is messy and unstructured. It conveys no meaning. One can find patterns and trends only when refined and presented in a certain way. Data visualisation helps in doing so. The process transforms an unrefined and raw dataset into a form that can be understood and provides insightful information. Thus, data visualisation enables data scientists to deliver the story they want to convey using the data.

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As with any form of storytelling, data visualisation requires creativity, design skills and the ability to explain the information most simply and efficiently as possible. Data analysts use several strategies to ensure that their data sends a clear and compelling message. One can learn these strategies as part of data analyst training.

Some of the best ways to improve data visualisation are listed below:

Decide the message you want to convey.

You should clearly know what you want your audience to take away from the visualisation. A dataset means nothing to a viewer until you give it meaning. Once you know what you want your data to stand for, you can design and present it that way.

Clean up and understand your dataset.

A thorough analysis of the dataset is essential before it can be visualised. Clean up your dataset and eliminate elements that do not contribute meaning, particularly when dealing with a large dataset. Only when you reach a conclusion based on the data can you convey it to others.

Choose the best graph type for your data.

Different charts and graphs are used to depict different forms of data. For instance, a line plot is used to show numerical trends over time, a bar graph is used to compare data values within the same category, and a pie chart shows the proportional distribution of data points within a specific category. Depending on your data and what you want to do with it, you can find which graph fits it best.

Keep things simple.

Choose simple charts and structure your data in a way that’s easy to understand. The better you know something, the more simply you can explain it.

Pay extra attention to the text.

You can neither make your visualisation completely text-free nor fill it with redundant text. Your visualisation usually has a title, label and legends. A title needs to be precise and easily understandable. Labels should also be simple and readable. Legends must be thought carefully as they should make understanding graphs easier.

Use colours in the right way.

Colours are an essential component of visualisation. Depending on how you use it, they can either enhance your visualisation or make it confusing. Avoid using too many colours and the same colour for the same data.

Keep your audience in mind.

The type of audience will impact the design of data visualisation. Depending on the audience’s familiarity with the subject, you will decide the level of explanation required and the design elements to be used. For instance, charts for internal stakeholders will look different from those designed for external audiences.

Reduce density.

Don’t try to cram everything on one chart. If there is too much information or too complex, illustrate it using more than one chart. Use whitespaces to help your readers focus and comprehend the information better.

Make use of dynamic visualisation.

Using dynamic visualisation can make your data highly insightful. Interactive visualisations where the viewers can remove some categories of data can help them understand the graph better. Gifs can also be used to depict the same base graph over different periods or conditions to demonstrate changes in the data.

Reduce dimensions.

When several dimensions are shown in the graph depicting different measurements for each data point, it will only confuse the audience. They will find it difficult to find or understand patterns. Thus, use a few dimensions to convey the meaning of your dataset.

Mention the source of the data.

Include the link to the original dataset to make your data visualisation as unbiased and objective as possible. Be honest about the assumptions you made, the data that was omitted and how you reached a particular conclusion.

The skill of data visualisation is essential to learn for data scientists. The data they gather and analyse can be of use to people only if appropriately designed.

At Imarticus Learning, we offer the perfect Data analytics course with placement. Our Postgraduate Program in Data Analytics and Machine Learning focuses on data visualisation with Tableau and PowerBI and other job-relevant skills. All graduates and professionals (up to 5 years of experience) with a technical background are eligible to apply.

Apply now to begin your journey as a data expert.

Top resources to learn python online in 2022

Posted on by Imarticus Learning

Python is currently the most demanding and one of the fastest-growing programming languages in the world. This is due to its versatility and numerous usages. Apart from it, Python is also user-friendly, and open-source and for beginners, who want to get into coding and data science, it is an excellent starting point.

Also, Python for data science, web development, and application development is in vogue, and its demand is not going to extinct soon. So, learning Python can provide plenty of employment opportunities in data science, machine learning, and artificial intelligence.

Top Resources to Learn Python Online

Individuals interested in learning about Python can easily do it online as a lot of resources are available on the internet. Although choosing the right way might be challenging for someone new to this field, a few of the best ways to get started are provided below – 

 

  • Online Courses 

 

One of the best ways to get python training online is to look for reliable online courses. However, make sure to opt for courses that give placement assistance and provide an explicit curriculum aligned with industry requirements. Individuals can also look up a self-paced course so that they can learn at their speed. Most importantly, check the curriculum and note if it meets the demands of the industry, such as Python, Data Analytics, and SQL. A PG Diploma in Data Analytics and Machine Learning usually covers all these topics and also provides a certification that acts as validation and can be produced for job opportunities. 

 

  • Data Analytics Bootcamps

 

Getting enrolled for a Bootcamp in data analytics focusing on Python is a great option to get started in this field. Also, acquiring a data analytics skillset with a focus on Python is an excellent resource as most industry demands an array of skills. This will allow students to work with complex data, and as Python is one of the fastest-growing languages, they can apply for a legion of jobs.

For instance, with data analytics and Python skills, individuals can apply for job roles such as business analysts, data scientists, and software developers. Apart from it, they can also learn machine learning with Python, which will likely open up a more comprehensive array of opportunities.  

 

  • business analytics certification courseIndependent Learning 

 

Lastly, individuals can opt for independent learning also from multiple platforms. There are also free resources available, and they can provide ample knowledge for those who want to learn. But, one of the primary disadvantages of these courses includes the lack of certification, and if they give a certificate, then they might not ensure job assistance.

Hence, to get python training online, it is better to opt for a course with certification validity. However, students must remember that these free resources can be a goldmine for those who want to expand their knowledge and whose sole purpose is to learn about data science. 

Apart from these, if individuals are still sceptical about learning Python, then they must understand the reasons for learning Python.

Reasons to Learn Python

Although individuals can learn machine learning along with Python and data science, the reasons to learn it are varied. The top reasons to know Python is – 

  • Python is easy to learn, read and write. 
  • It is required for high-demand jobs. 
  • Python is highly flexible and has myriad use. 
  • It also has a large supportive community. 

Hence, individuals can learn Python for machine learning and data science to get a multifaceted approach. This will ensure varied skills and open up new job opportunities for them. Also, while choosing the path to learn, keep the goals lucid to clear the way. But, to get a job opportunity, students can opt for a course with certification that validates the course and knowledge. 

Understanding regularization in machine learning

Posted on by Imarticus Learning

A machine learning model is a set of algorithm expressions that understands and analyses mounds of data to make predictions. Why is it that sometimes a machine learning model does great on training data but not so well on unseen data? It happens because, at times, this model becomes an overfitted model or even an under-fitted one.

Data fitting is very crucial for the success of this model. In this model, we plot a series of data points and draw the best line towards the relationship between the variables. 

This model becomes an overfitting one when it gathers the details with the noise present in the data and tries to fit them on the curve. 

The underfitting model neither learns the relationship between variables nor classifies a new data point. At Imarticus, we help you learn machine learning with python so that you can avoid unnecessary noise patterns and random data points. This program makes you an Analytics so you can prepare an optimal model. 

Meaning and Function of Regularization in Machine Learning

When a model becomes overfitted or under fitted, it fails to solve its purpose. Therefore, at Imarticus, we teach you the most crucial technique of optimal machine learning. In this program, we coach you to become an Analytics by learning the procedures to add additional information to the existing model. 

In the regularisation technique, you increase the model’s flexibility by keeping the same number of variables but at the same time reducing the magnitude of independent variables. This technique gives flexibility to the model and also maintains its generalization.

Regularization Techniques

The regularization techniques prevent machine learning algorithms from overfitting. It is possible to avoid overfitting in the existing model by adding a penalizing term in the cost function that gives a higher penalty to the complex curves. Regularization reduces the model variance without any substantial increase in bias. Python classes also help in this technique.

To become an Analytics, you have to understand these two main types of regularizations:

  • Ridge Regression
  • Lasso Regression

Ridge Regression:

In this type of regularization, we introduce a small amount of Ridge regression penalty bias for a better long-term prediction. It solves the problems when the parameters are more than the samples. It decreases the complexity of the model without reducing the number of variables. Though this regression will shrink the coefficients to the least dominant predictors, it will never make them zero.

Lasso Regression:

In this regularization technique, we reduce the complexity of the model. The penalty weight includes the absolute weight in the Least Absolute and Selection Operator. The coefficient estimate equals zero, and it provides the feature selection. 

But, if predictors are more than the data points, this model will pick non-zero predictors. This model also selects the highly collinear variables randomly. 

Data Analytics Certification 

The certification in AIML will train you as an Analytics. It will help you understand how regularization works. After completing the certification program at Imarticus, you will know to shrink or regularise the estimates to zero. You can also enhance the model that can accurately calculate the data.

Why problem-solving using Data Analytics needs new thinking?

Posted on by Imarticus Learning

Data analytics helps organizations examine all sorts of data, be its real-time, historical, unstructured, structured, or qualitative data to uncover trends and develop insights that may be used to guide and, in some circumstances, automate decisions, thus connecting intelligence and action.

best data analytics certification courses in IndiaThe best solutions now enable the entire analytical process, from data access, preparation, and analysis through analytics operationalization and evaluating results. Hence, there is a rise in data analytics courses in India.

Data analytics enables firms to digitally transform their company and culture, allowing them to make more innovative and forward-thinking decisions. Algorithm-driven firms are the emerging innovators and business leaders, going beyond typical KPI monitoring and reporting to uncover hidden patterns in data.

In the present scenario, many firms have had to adjust their emphasis, review their existing timeframes, and rethink how they function. Companies that prioritized the digitalization of their business, particularly investments in analytics and automation, have fared the best during this crisis. Nonetheless, AI continues to be a source of consternation for many business leaders across industries.

Why is data analytics used to solve business problems?

Businesses sometimes overlook the fact that the most complicated business issues can be solved using analytics by utilizing the most recent data analytics solutions. It is critical to understand that the analyst spends approximately 70% – 80% of his or her time generating analytical files.

Only the remaining 20-30% is spent on developing a solution. Predictive models or simple business rules are used in this approach to solution creation. A corporation can use analytics tools to extract simple structured data and reduce its effort in retrieving unstructured and semi-structured information.

The rising need for an analytical solution has compelled businesses to invest in analytics solutions that will allow business users and employees across the enterprise to get the answers they need. Businesses can use these powerful tools to perform complex analytics without the need for programming assistance from data scientists. As a result, businesses will gain a new competitive advantage and the ability to unearth previously undetected patterns, propelling them to a position of influence.

Today’s problem solving necessitates a new outlook in order to obtain these insights quickly and efficiently, and there is a need for a cohesive technological change to displace the conventional data analytics tools and dissolve the walls between business groups.

Technology behemoths have demonstrated what can be accomplished when data and analytics are at the core of a business model. Needless to mention, the world’s most successful firms are all data-driven, with their focus on leveraging data to understand, market to, and increase income from their customers. This cultural shift of democratization of access to data and analytics across an organization has allowed these organizations to capitalize on the data economy and drive digital transformation.

Analytic Process Automation, or APA, is a new category that could be the key to capturing the best of man and machine at scale. APA automates business processes and provides immediate self-service access to business-critical data insights to even the most inexperienced knowledge workers. In reality, this implies that more employees will be able to use as well as gain from data with hardly any training.

This, in turn, alleviates the long-standing friction between data specialists and business managers, the latter of whom has relied on the former’s access to critical information. APA de-emphasizes data analytics at a level that the business sector has never seen before.

Conclusion

Data Science as we all know is emerging as an extremely important profession in the present scenario and it is important to have a data analytics certification to be an expert in this field. There are good data analytics courses in India that provide data analytics certification and will also increase your career prospects if you wish to pursue it further.

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

Posted on by Imarticus Learning

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

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

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

Key trends in customer engagement using analytics:

Growth is likely to continue:

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

Analytics going to be the main driving force:

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

Analytically experienced are using more data:

data analytics courses in IndiaAnalytically experienced organizations tend to use more data from all possible sources when compared to lesser experienced organizations.

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

Key points for better customer mapping

Data source and data types:

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

Integrated system:

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

Innovation to turn customer mapping into customer satisfaction

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

Building profiles using Analytics

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

Importance of Qualitative data

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

Prioritization of personalization

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

Conclusion

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

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