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Tag: best data analytics courses with placement in India

How Data Science is Making Personalization of Customers Feasible?

Posted on by Imarticus Learning

How Data Science is Making Personalization of Customers Feasible?

Data science opens the door to an enormous number of possibilities in customer experience management. It plays an increasingly important role in all areas of the customer relationship management lifecycle, but countless companies have yet to make this advanced technology part of their marketing tools.

One of the main reasons is the lack of full visibility of what can help them engage better with customers and the inability to quantify potential improvements. Nowadays, with the amount of information available to both consumers and businesses, the key to success is knowing how to offer personalized offers that appeal to each consumer. 

Data Science for the Hypersonalization of Customers

To better understand how data science can make sales and marketing actions more effective, it helps to think about one of the main responsibilities of these groups: acquiring new customers. To optimize commercial strategies in a highly competitive market, working around qualified leads is the basis for success. In that sense, data science can greatly improve projections and help a company increase sales by effectively identifying those who represent real business opportunities. 

Intelligent data analysis allows the segmentation of leads based on their specific criteria, such as needs, purchasing power, geographic location, and other exclusionary criteria. In this way, it is possible to optimize prospecting efforts, allowing companies to increase their closing rates and, ultimately, business profitability. 

Role of Data Science

Data science extracts value from data through the combination of multiple fields, such as statistics, artificial intelligence, and data analytics. Data science involves the preparation of data for analysis, including steps such as data gathering, scrubbing, presentation, and manipulation. Data scientists can pursuit analytical operations and are able to review results to reveal patterns and enable businesses from different fields to gain informed insights.

To optimize commercial strategies in a highly competitive market, working around qualified leads is the basis for success. In that sense, data science can greatly improve projections and help you increase sales by effectively identifying those who represent real business opportunities. Today, more and more people are opting for a Data Scientist Career, as it is in increasing demand in many industries.

Why Imarticus for data science online course?

Not only is data science being key for market forecasting and finding good investment opportunities but also for smart marketing. As competition in the market increases, it is becoming more and more necessary to shape the business according to the demands of end-users. Data science makes it possible to offer products/services that address the needs of each user. 

Here at Imarticus, we offer an industry-designed curriculum on DSP Data Science Prodegree. In partnership with many industry leaders, we will introduce you to real business projects and case studies, throughout high-quality tech-enabled education. With one of our courses at Imarticus, not only will you learn data science, but also, we will provide you full placement upon completion of the program.

Conclusion

Data science opens a door to an enormous number of possibilities in customer experience management. It gives sales and marketing professionals a new way to make key data-driven decisions on how to deploy resources and engage prospects and leads more effectively, eliminating the reliance on guessing answers and relying on gut instincts in making critical decisions. You can subscribe to a data analytics course in India offered by Imarticus and become a well-profiled professional in this field! 

How are Business Risks Predicted using Logistic Regression?

Posted on by Imarticus Learning

Logistic regression is a mathematical technique that estimates the probability of an event occurring. Using historical data to create a predictive model, you can use regression to predict business, investment, operational, and strategic risks. By understanding how these risks get indicated, you can better assess your company’s vulnerabilities and protect them from future losses.

This blog post will provide examples of how you might use regression in your workplace and explain what this technique does in more detail.

Why is Logistic Regression critical?

It is a statistical technique that tries to understand how the probability of an event occurring changes when one or more variables get altered. The method builds predictive models using data about previous incidents to use for proactively predicting future events. For instance, you could use regression to guess which customers are most likely to stop using your products and services.

Logistic regression can use to predict business risks in many ways, including:

  • Identifying the likelihood of a bad debt written off.
  • Assessing the probability that an IT system will cause downtime.
  • Estimating the risk that a new product or service will flop.

For example, suppose you are assessing the risk that a customer will default on their repayments. In that case, your model might include variables such as the loan amount and the borrower’s age. If you are trying to assess IT downtime risk, some variables might be how old a system is and its many users.

  • Assessing internal risk levels by quantifying how much staff turnover there has been over the past year. By using information about the average time, it takes for employees to complete their tasks.

For example, suppose you are trying to determine which product is most profitable. If you are trying to assess how quickly tasks are completed, some variables might be how long a study takes to complete and how many times it has met before.

  • You can use it to quantify the risk that you will not receive payment for goods or services supplied.
  • Assessing the likelihood of a customer is likely to leave your company’s favor based on variables. Such as their tenure, monthly spending, and how many requests they have made for support.
  • Predicting the probability of a new product being successful.
  • It determines the likelihood of a new employee bringing in a valuable new business.

Explore and learn with Imarticus Learning

This PG program is for industry professionals to help students master real-world applications from the ground up. Therefore students can construct strong models to provide meaningful business insights and forecasts.

This program is for recent graduates and early-career professionals who want to further their careers in Analytics, the most in-demand job skill. With this program’s job assurance guarantee, students may take a significant step forward in their careers.

Some course USP:

  • Risk management courses aid the students in learning job-relevant skills that prepare them for an exciting financial market career.
  • Impress employers & showcase skills with a certification endorsed by India’s most prestigious academic collaborations.
  • World-Class Academic Professors to learn from through live online sessions and discussions. It will help students understand the 360-degree practical learning implementation with assignments.

Understanding the Differences between Linear & Logistic Regression

Posted on by Imarticus Learning

Linear regression is one of the most common types of statistical models used in data prediction. It’s easy to use, and it assumes that a straight line can express a relationship between two variables. Logistic is another type of statistical model that also makes predictions.

Still, it does so using a logarithmic function rather than a linear function. This post will explain how these two methods differ and give you some practical applications for them both.

What is Linear Regression?

The linear discriminant analysis uses when predicting one variable based on another. This model assumes that if the slope of the line changes, so does the value of the predictions. It fits a straight line to your statistics using an ordinary least squares method. If you have more than two variables, consider using multivariate instead.

What is Logistic Regression?

Suppose you have an extensive collection of data that includes three different variables, one of which is the outcome you’re trying to predict (label). Using Linear might not make sense if your result isn’t directly related to the other two variables (independent variables or features).

Logistics can easily handle non-linear relationships between the label and the elements. For example, if you’re trying to predict if an email is a spam or not. The probabilities generated by logistics can work even if your features are unrelated to each other.

The Differences between Linear & Logistic Regression

LINEAR REGRESSION LOGISTIC REGRESSION
It is a simple and robust method based on linear algebra. It is one of the most well-known machine learning algorithms.
It assumes that the relationship between features and the outcome is linear, making it ideal for modeling or analyzing existing relationships in data. It is for binary classification problems, but you can also use it to solve multi-class classification problems by using an approach called “one versus all.”
You can, however, add interactions between variables to create non-linear models such as quadratic functions. You can not add interactions between variables to create non-linear models.
Unlike logistic, linear doesn’t require the outcome to be known beforehand.

 

 Logistic gets considered a supervised algorithm because you must provide the predicted (outcome) label beforehand.
 It is excellent at handling these events because it only cares about the relationship between features and outcomes. Logistic, on the other hand, is designed for discrete data (categorical or binary). It can’t handle these cases effectively because of how the sigmoid function works.

 

The table summarizes some of the critical differences between linear and logistic. Notice that, like all machine learning models, both algorithms are affected by outliers or data points outside the normal range (tails) that generate significant errors.

Explore and learn with Imarticus Learning

Learn the fundamentals of data analytics and machine learning and the most in-demand data science tools and methods to become job-ready. With Tableau, a student can learn Python,  SQL, Data Analytics, Machine Learning, and data visualization. The curriculum includes Capstone Projects, real-world projects, relevant case studies, and mentoring to assist students in becoming a master in their fields.

Some course USP:

  • Data science courses in India aid the students in learning job-relevant skills that prepare them for an exciting data scientist career.
  • Impress employers & showcase skills with a certification endorsed by India’s most prestigious academic collaborations.

World-Class Academic Professors to learn from through live online sessions and discussions. It will help students understand the 360-degree practical learning implementation with assignments.

Contact us through the live chat support system or schedule a visit to Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, and Gurgaon training centers.