Data Science for CRM

Are you aware of the ways data science can elevate your business? If not, it’s time to find out. You will be amazed at the positive effects this powerful tool can have on your organization

Data Science for CRM

Introduction to Data Analytics

Data analysis is a multi-step process including examining, cleaning, exploring, and transforming data to discover useful information, and support decision-making. This part will give you an introduction to some basic tools (Jupyter Notebook, Google Colab.) and libraries (Numpy and Pandas) that you will use throughout the bootcamp.

Introduction to Python

Python is the favorite programming language among data scientists, and it is not a coincidence. Large communities and libraries, ease of use, open source, flexibility, and integration with other programming languages are just some of the advantages of Python. In this part, you will start from the basics of Python such as data structures and you will be able to run your independent analysis using it.


Unlock the potential of Python as you explore various data types and gain hands-on experience through practical examples and exercises. Whether you're a beginner or looking to expand your coding skills, this content is your gateway to becoming a confident Python programmer.

Data Exploration

Discover why data exploration is the heartbeat of every successful data science project. It goes beyond mere data collection and dives deep into uncovering the secrets hidden beneath the surface. From identifying outliers and missing values to identifying trends and making data-driven decisions, this dynamic process holds the key to transforming raw data into actionable insights.


As we embark on a transformative data journey, where data exploration reigns supreme. Witness firsthand how it fuels innovation, drives business growth, and empowers decision-makers to make informed choices.

Introduction to SQL

The relational database is a part of the day-to-day work of a data scientist. SQL (Structured Query Language) is a programming language used to manage and manipulate relational databases. This part will help you to understand basic queries, aggregations, joins, subqueries, and case statements. To do that you will use PostgreSQL.

Introduction to Statistics

Statistics is an essential part of data science. A good data scientist should combine, along with other hard and soft skills, coding, and statistical knowledge to come up with a reliable model.


This course covers the essentials of statistics, including the foundations, key concepts, and practical applications. Delve into the world of distributions and probabilities, gaining insights into various probability distributions and their real-world significance. With hands-on exercises and examples, you'll develop the skills to analyze and interpret data with confidence.

Experimental Design

As a data scientist, you will have to design many experiments. So, you can think of this part as a warm-up for your real challenges. Having learned the main statistical test such as the t-test and A/B test, you will be ready to run your data-dependent project.

First Capstone

Up to this point, you learn many things and now it is time to present your skills. In the first capstone, you will work on a project that will be assigned to you by Leveragai. This project will be mainly based on data exploration, and it also helps you to sharpen your Python, statistics, and analytical skills.

First Mock Interview

To be able to prepare you for the real data science interview, we created two mock interviews during this program. The mock interview is held by an industry expert with a 1-on-1 session. You will be asked several questions about the topics you have covered so far. This is the first one and you must take and pass it to move forward. This first mock interview is designed to compare the level of knowledge you have and the one you are supposed to have. You will be asked coding, statistics, and design questions.

Introduction to Machine Learning

Here comes the part that we have prepared so far. This is the very first course in machine learning. In this part, you will learn the basic but most important concepts of machine learning that you will refer to along the way. These concepts include but are not limited to introduction to machine learning and supervised/unsupervised learning, train-test split, cross-validation, overfitting, and bias-variance trade-off.

Supervised Learning Models

Regression models are statistical models that are used to predict a numerical value. This part of the curriculum will give an in-depth understanding of regression-based modeling. You will first learn the theory of these models and see how you can apply it using Python.  Having talked about over and underfitting. Regularization will be discussed via the ridge, lasso, and elastic net regression.

Under supervised learning title, you will also learn classification models that are  types of machine learning models used in data science to predict the categorical class or label of an input data point. Having categorical classes in the data is not something rare, so it is better to get used to applying classification models. 

There are several types of machine learning models that can be applicable to classification and regression and, after discussing linear regression, you will be familiar with logistic regression, decision trees, random forests, and support vector machines, and boosting algorithms.

Second Capstone

Having completed the regression and classification models, now you know how to run a machine learning model. Combining your data exploration, Python, and machine learning modeling knowledge, you are all set to run your first machine learning modeling project as a second capstone. Leveragai will provide the project and you will apply your skill to tackle it.

Unsupervised Learning

What happens if data does not include any labels? It means you should run an unsupervised learning model. An unsupervised learning algorithm can be used for clustering, anomaly detection, and dimension reduction. In this part, you will learn how unsupervised learning models work. As always, you will learn the theory of KMeans, DBSCAN, and hierarchical clustering methods and apply them.

Data Science for CRM - 1

This is the very first part of your specialization in CRM. This part includes, but is not limited to, an introduction to CRM analytics, creating stories with visualization, and correlation and parametric and non-parametric independence tests.


In the introduction to CRM part, you will learn how and why we need CRM analytics as well as important tools used in CRM analytics. What comes next is to tell a story using visualization. It is quite important, especially when sharing your findings with non-experts such as your clients or executives.


This part concludes with correlation and independence tests. As CRM analytics is all about delighting your customers, companies try different campaigns to achieve whatever their goal is. To properly design these experiments, there are certain statistical tools that one needs to be familiar with. In the final section of the first part, you will learn effective ways to use these tools.

Data Science for CRM - 2

This part is devoted to broad and important topics. P&L analysis and predictive analytics Both are incredibly broad and important topics and are the cornerstones of data-driven decisions. As a CRM data scientist, these two topics are of paramount importance and will be essential in helping you succeed in this field.


In P&L analysis, you will learn how to analyze financial statements and find the most important as well as outlier items in these statements. This analysis enables you to detect effective and ineffective parts of the unit (branch, restaurant, hotel, etc.).


Predictive analytics is a set of techniques that a CRM data scientist must have at her disposal. Predictive analytics is so broad that you need to restrict our attention. So, you will learn time series analysis to forecast future sales and customer lifetime value as a part of predictive analytics.

Data Science for CRM - 3

The final part of the CRM specialization is devoted to sentiment analysis and churn analysis.


Sentiment analysis helps you process and better assess customer feedback. This is the backbone analysis for improving the customer experience. You will run a sentiment analysis using customer comments.


What comes next is the churn analysis. In today's competitive world, it is quite important and less costly to keep your customers engaged. However, it is a challenging task. Churn analysis provides you with a unique tool to better understand and predict customer behavior so that you can initiate and restructure your marketing campaign.

Final Capstone

An important part of a data scientist’s job is to bring research questions and in this third and final capstone, you will see how a real research question can be emerged and be tackled. So, you will first work on the research question that you want to address, check if data is available, and, upon approval of your mentor, you will start working on it.

Second Mock Interview

In this second mock interview, you will be tested on what you have learned so far with a 1-on-1 session. An industry expert mentor will ask you several questions and gauge your level of knowledge and provide you feedback so that you better understand if you are ready for the real data science interviews.


Congrats! Now, you will be paired with a company to accomplish a data science or analytics project. During this internship, you will equip yourself not only with theoretical skills but also with real-life challenges. This internship will take at least three months.


Once you have finalized all the steps given above, you deserve to graduate. A certification will be awarded to you by Leveragai.

Start Today

Data Science for CRM

Best Offer

$4900 $3900 / one time
  • Unlimited access to all materials
  • Move forward on your own schedule
  • Get your hand dirty with CRM focused industry projects
  • Learn from industry experts with 1-on-1 sessions
  • Explore the magical CRM implementations in data science
  • Get your certification

The Importance of Familiarity with Customer Relationship Management in Data Science



Customer Relationship Management (CRM) is a critical component of any business strategy, and its significance in the field of data science cannot be overstated. In order to successfully leverage data science projects and drive meaningful insights, it is crucial for data scientists to have a deep understanding of the technical details and intricacies of CRM. By familiarizing themselves with the core elements of CRM and its application in data-driven decision-making, data scientists can unlock the full potential of customer data and deliver exceptional results.

Understanding CRM in the Context of Data Science

CRM encompasses a range of activities and technologies that enable businesses to manage and nurture relationships with their customers. It involves the collection, analysis, and interpretation of customer data to gain insights into customer behavior, preferences, and needs. Data science, on the other hand, focuses on extracting actionable insights from large volumes of data using various statistical and analytical techniques. By combining the principles of CRM with data science methodologies, organizations can enhance their customer-centric strategies and drive business growth.

Leveraging Customer Data for Data Science Projects

Customer data is a goldmine of information that can fuel data science projects and provide valuable insights. By analyzing customer interactions, transactions, and preferences, data scientists can uncover patterns, trends, and correlations that can inform business strategies and decision-making processes. For example, data scientists can use CRM data to identify customer segments, predict customer churn, personalize marketing campaigns, optimize pricing strategies, and improve customer experience.

Leveraging customer data for data science projects has emerged as a cornerstone in modern business strategies, propelling organizations into a new era of informed decision-making and personalized experiences. In an age where data is often referred to as the “new oil,” harnessing the wealth of customer information generated through interactions, transactions, and engagements has become a transformative force. Customer data, encompassing demographic details, purchase histories, browsing behaviors, social interactions, and more, serves as a treasure trove of insights that can shape marketing campaigns, product innovations, and service enhancements.

By meticulously analyzing and modeling customer data, organizations gain a profound understanding of consumer preferences, patterns, and needs. Data scientists equipped with advanced analytical techniques, including machine learning and predictive modeling, can uncover hidden trends and correlations that are crucial for anticipating market shifts and staying ahead of competition. Through these data-driven insights, businesses can tailor their offerings, optimize pricing strategies, and devise targeted marketing campaigns that resonate with specific customer segments, ultimately boosting customer engagement and loyalty.

Moreover, the amalgamation of customer data with data science techniques facilitates the creation of personalized experiences that were once thought unattainable. By employing recommendation systems, chatbots, and sentiment analysis, companies can interact with customers on a one-on-one basis, understanding their unique requirements and preferences. This not only enhances customer satisfaction but also cultivates a sense of brand affinity, turning customers into advocates who willingly spread positive word-of-mouth and contribute to organic growth.

However, the utilization of customer data for data science projects is not without its ethical considerations. Striking a delicate balance between extracting meaningful insights and safeguarding individual privacy is paramount. Organizations must adhere to stringent data protection regulations, ensuring that customer information is anonymized, secured, and used transparently. In doing so, they build trust with their customer base and foster a long-term relationship built on integrity.

In essence, leveraging customer data for data science projects embodies the convergence of technological prowess and customer-centricity. It empowers organizations to decode the intricate tapestry of consumer behavior, offering a compass that guides them through a competitive landscape. As data science continues to evolve, the strategic utilization of customer data stands as a testament to the power of insights and innovation in shaping the future of businesses across industries.

The Role of Big Data in CRM

The advent of Big Data has revolutionized the way businesses approach CRM. With the proliferation of digital touchpoints and the increasing volume, velocity, and variety of customer data, organizations now have access to vast amounts of information. Data scientists play a crucial role in harnessing this data and transforming it into actionable insights. Through advanced analytics techniques, such as machine learning and predictive modeling, data scientists can extract valuable insights from Big Data to optimize CRM strategies and deliver personalized customer experiences.

The Benefits of CRM-Driven Data Science Projects

Integrating CRM with data science projects offers several key benefits for businesses. Firstly, it enables organizations to gain a holistic view of their customers by consolidating data from various sources, such as sales, marketing, and customer service. This comprehensive view allows businesses to understand customer behavior, preferences, and needs at a granular level, enabling them to tailor their offerings and communications accordingly.

Secondly, CRM-driven data science projects enable organizations to improve customer retention and loyalty. By analyzing customer data, data scientists can identify early warning signs of customer churn and develop proactive strategies to retain valuable customers. These strategies may include targeted marketing campaigns, personalized recommendations, or improved customer service initiatives.

Furthermore, CRM-driven data science projects can enhance the effectiveness of marketing and sales efforts. By leveraging customer data, organizations can identify high-value customer segments, optimize marketing spend, and deliver targeted messages to the right audience at the right time. This level of personalization can significantly improve customer engagement and conversion rates.

The Role of AI and Machine Learning in CRM

Artificial Intelligence (AI) and machine learning algorithms are revolutionizing CRM by enabling organizations to automate and optimize various aspects of customer management. AI-powered chatbots, for example, can provide instant customer support, while machine learning algorithms can analyze customer data to predict customer behavior and anticipate their needs. Leveraging AI and machine learning in CRM can streamline business processes, enhance customer experiences, and drive operational efficiency.

Leveragai’s CRM-Based Data Science Bootcamp

For professionals looking to enhance their expertise in CRM-driven data science projects, LeveragAI offers a comprehensive CRM-based data science bootcamp. LeveragAI’s bootcamp provides hands-on training in CRM technologies, data analysis, machine learning, and predictive modeling. With a 1-on-1 mentored approach, participants gain practical experience in leveraging CRM data for data science projects and develop the skills needed to excel in the field.


Familiarity with CRM is crucial for data scientists embarking on data science projects. By understanding the technical details and core principles of CRM, data scientists can unlock the full potential of customer data and drive meaningful insights. Leveraging CRM data in data science projects enables organizations to improve customer retention, enhance marketing and sales efforts, and deliver personalized customer experiences. With the advent of Big Data, AI, and machine learning, CRM-driven data science projects are becoming increasingly powerful and indispensable for businesses. By integrating CRM and data science, organizations can gain a competitive advantage and drive business growth in today’s data-driven world.

To learn more about LeveragAI’s CRM-based data science bootcamp and how it can help you enhance your expertise in CRM-driven data science projects, please visit LeveragAI

Frequently Asked Questions (FAQ)

The Leveragai Data Science for CRM Bootcamp is a specialized program designed to equip participants with the skills and knowledge required to excel in data science within the field of Customer Relationship Management.

The bootcamp is an intensive 5-month program, carefully structured to provide a comprehensive understanding of data science applications in CRM.

The bootcamp encompasses various CRM domains, including customer segmentation, churn prediction, personalized marketing, sales forecasting, and customer sentiment analysis.

 This bootcamp stands out by combining data science expertise with CRM domain knowledge. It focuses on practical projects and challenges relevant to real-world CRM scenarios.

There are no prerequisites for the Bootcamp. So, the Bootcamp is suitable for everyone interested in leveraging data science within the CRM domain.

You can apply for the bootcamp by visiting this page on Leveragai’s website. Click on “ADD TO CART” and follow the steps.

Throughout the bootcamp, you will have access to experienced mentors on a weekly basis. Regular mentoring sessions and interactive discussions will enhance your learning experience. Also, Leveragai organize regular meetup and hosts industry professionals to discuss latest improvement in data science for CRM.

After successful completion, you’ll be well-prepared for roles such as CRM analyst, marketing analyst, customer insights manager, or data scientist in CRM-focused industries.

Accordion Content

Yes, we offer a satisfaction guarantee. If you’re unsatisfied within 2 weeks, you can request a refund. Refer to our refund policy for detailed information.