This Data Science course, in collaboration with IBM, accelerates your career in Data Science and provides you with world-class training and skills required to become successful in this field. The Data Science course offers extensive training on the most in-demand Data Science and Machine Learning skills with hands-on exposure to key tools and technologies including Python, R, Tableau, and concepts of Machine Learning. Become an expert in Data Science by diving deep into the nuances of data interpretation, mastering technologies like Machine Learning, and mastering powerful programming skills to take your career in Data Science to the next level.
This joint partnership between Simplilearn and IBM introduces students to an integrated blended learning approach, making them experts in data science. This Data Science course, in collaboration with IBM, will help students become industry-ready for top data scientist job roles.
What can I expect from this Data Science course developed in collaboration with IBM?
Upon completion of this Data Science Certificate course, you will receive IBM certificates for the IBM courses and Simplilearn certificates for all the courses in the learning path. These certificates will testify for your skills and assert your Data Science expertise. You can also avail the following benefits as part of this Data Science online course:
Data Scientist is one of the hottest professions of this year. The United States Bureau of Labor Statistics sees unprecedented growth in the field of data science and predicts 11.5 million job openings in 2026 rising at an annual growth rate of 28%. Simplilearn's Data Science certification course co-developed with IBM encourages you to master job-critical skills including statistics, hypothesis testing, data mining, clustering, decision trees, linear and logistic regression, data wrangling, data visualization, regression models, recommendation engine, supervised and unsupervised learning and much more.
This Data Scientist course offers a comprehensive curriculum combining live-online instructor-led classes and self-paced learning videos co-developed with IBM. This Data Science certification training concludes with a Capstone project designed to reinforce the learning by building an industry-relevant product encompassing all the key aspects learnt in the Data Science Training program. The skills learnt in this Data Science course will help prepare you for the role of a Data Scientist.
A Data Scientist is a top-ranking professional in any organization. Glassdoor ranks Data Scientist 3rd in the 50 Best Jobs for 2022. In today’s ever-changing market, Data Scientists are scarce and in high demand. As a Data Scientist, you are required to understand business problems, design data analysis strategies, collect, cleanse and structure the required data, apply algorithms and techniques using the right tools, and make recommendations backed by data.
In this Data Science course, you will:
This Data Science certification training includes 25+ industry-relevant projects from various domains to help you master concepts of Data Science. A few of the projects that you will be working on are mentioned below:
Capstone Project:
In this Data Science Course, You will go through dedicated mentor classes in order to create a high-quality industry project, solving an industry-relevant problem leveraging the skills and technologies learned throughout the Data Science online course. The capstone project will cover all the key aspects of data extraction, cleaning, and visualization to build and tune data models. You also get the option of choosing the domain/industry dataset you want to work on from the options available.
After successful submission of the project, you will be awarded a capstone certificate for Data science that can be showcased to potential employers as a testament to your learning.
Course End Projects
Projects that mimic real-world business problems to help apply the concepts learned during a specific course. Typically these projects take 3 - 4 hours to complete
Projects are as follows:
BUILDING USER BASED RECOMMENDATION MODEL FOR AMAZON
Domain: E-commerce
The dataset provided contains movie reviews given by Amazon customers. Perform data analysis on the Amazon customer movie reviews dataset and build a machine learning recommendation algorithm that provides the ratings for each of the users.
COMCAST TELECOM CUSTOMER COMPLAINTS
Domain: Telecommunications
Comcast is an American global telecommunication company. The firm has been providing terrible customer service. They continue to fall short despite repeated promises to improve.Utilise the existing database of customer complaints as a repository to improve customer satisfaction.
MERCEDES-BENZ GREENER MANUFACTURING
Domain: Automobile
Reduce the time a Mercedes-Benz spends on the test bench. Work with a data set representing different permutations of features in a Mercedes-Benz car to predict the time it takes to pass testing. Optimal algorithms will contribute to faster testing, resulting in lower carbon dioxide emissions without reducing Mercedes-Benz’s standards.
RETAIL ANALYSIS WITH WALMART
Domain:Retail
One of the leading retail stores in the US, Walmart, would like to predict sales and demand accurately. The business is facing a challenge due to unforeseen demands and runs out of stock occasionally. It’s discovered that a machine learning algorithm is at the core of this issue. Build an ideal ML algorithm that will predict demand accurately and incorporate factors like economic conditions including CPI, unemployment index, etc.
MOVIE LENS CASE STUDY
Domain: entertainment
Perform analysis using the exploratory data analysis technique. You need to find features affecting the ratings of any particular movie and build a model to predict the movie ratings.
CUSTOMER SERVICE REQUESTS ANALYSIS
Domain: Customer Service
Perform data analysis on New York City 311 service request calls. You will focus on data wrangling techniques to understand data patterns and also create visualizations to categorize and prioritize complaint types like economic conditions including CPI, Unemployment Index, etc.
COMPARATIVE STUDY OF COUNTRIES
Domain: Geo-political
Create a dashboard to do a comparative study on various parameters of different countries using the sample insurance data set as well as the world development indicators data set.
SALES PERFORMANCE ANALYSIS
Domain: Retail
Build a dashboard that will present monthly sales performance by product segment and product category to help clients identify the segments and categories that have met or exceeded their sales targets, as well as those that have not met their sales targets.
PREDICT THE DEMAND OF LOAN BASED ON REGION
Domain: Banking
This project provides learners with insights into the banking sector. Learners are required to build a statistical model to predict the demand of loans for a particular region. To show the results, learners are required to provide an online dashboard that shows the plan, and its progress, to all stakeholders.
BUILD MODEL TO PREDICT DIABETIC PATIENTS
Domain: Healthcare
The project is aligned with NIDDK (National Institute of Diabetes and Digestive and Kidney Diseases) data sets representing one of the most chronic and consequential diseases. The goal of this project is to build a model to predict patients with diabetes by utilizing the given data set.
CUSTOMER SEGMENTATION ON RETAIL CUSTOMERS
Domain: Retail
Perform customer segmentation using RFM analysis. The resulting segments can be ordered from most valuable (highest recency, frequency, and value) to least valuable (lowest recency, frequency, and value)
The Data Science role requires an amalgam of experience, data science knowledge, and correct tools and technologies. It is a solid career choice for both new and experienced professionals. Aspiring professionals of any educational background with an analytical frame of mind are most suited to pursue the Data Science certification course, including:
Kickstart your learning of Python for Data Science with this introductory course and familiarize yourself with programming, tastefully crafted by IBM.
This Data Science with Python certification course gives you a complete overview of Python’s data analytics tools and techniques. Learning python is a crucial skill for many data science roles, and you can develop it with this Python data science course. With a blended learning approach, you can learn Python for data science along with concepts like data wrangling, mathematical computing, and more. Unlock your career as a data scientist with Simplilearn’s Data Science with Python training.
Ensure career success with this Machine Learning course. Learn this exciting branch of Artificial Intelligence with a program featuring 58 hrs of Applied Learning, interactive labs, 4 hands-on projects, and mentoring. With our Machine Learning training, master Machine Learning concepts are required for a Machine learning certification. This Machine Learning online course will provide you with the skills needed to become a successful Machine Learning Engineer today.
This Tableau certification course helps you master Tableau Desktop, a world-wide utilized data visualization, reporting, and business intelligence tool. Advance your career in analytics through our Tableau training and gain job-ready skills. Tableau certification is highly regarded by companies for data-related jobs and our Tableau online course trains you to use the tool effectively for preparing data, creating interactive dashboards, adding different dimensions, and drilling into outliers.
Simplilearn’s Data Science Capstone project will give you an opportunity to implement the skills you learned in the Data Science certification course. Through dedicated mentoring sessions, you’ll learn how to solve a real-world, industry-aligned Data Science problem, from data processing and model building to reporting your business results and insights. The project is the final step in Data Science training and will help you to show your expertise in Data Science to employers.
Our Data Scientist course is exhaustive and this certificate is proof that you have taken a big leap in mastering the domain.
The knowledge and Data Science skills you've gained working on projects, simulations, case studies will set you ahead of the competition.
Talk about your Data Science certification on LinkedIn, Twitter, Facebook, boost your resume, or frame it - tell your friends and colleagues about it.
Data science is a broad field that involves dealing with large volumes of data to uncover hidden trends and patterns and extract valuable information that aids in better decision-making. As companies are collecting massive amounts of data, they use various data science tools and techniques to build predictive models. Simplilearn’s Data Science training can help you learn all of its concepts from scratch.
In a Data Science course, you need to learn about so many concepts if you are a beginner or an intermediate. A Data Science course is a training program of around six to twelve months, often taken by industry experts to help candidates build a strong foundation in the field. Apart from the theoretical material, our online Data Science certification course includes virtual labs, industry projects, interactive quizzes, and practice tests, giving you an enhanced learning experience.
A Data Scientist is an individual who gathers, cleans, analyzes, and visualizes large datasets to draw meaningful conclusions and communicate them to the business leaders. The data is collected from various sources, processed into a format suitable for analysis, and fed into an analytics system where a statistical analysis is performed to gain actionable insights.
Data scientists are responsible for analyzing large amounts of data, visualizing them to find hidden trends and patterns, and drawing meaningful insights. Such actionable insights aid in solving complex business problems and making better decisions. They apply data science techniques like exploratory data analysis, statistical modeling, and machine learning to uncover hidden correlations in data. This Data Science course can help you become capable of handling all these responsibilities.
A question that I often hear from clients and colleagues is, "Why should I get a Data Science certification?" That is a fair question for most other areas of study and business. In areas such as finance or engineering, there are far more important accreditations you could and should achieve before “hanging your shingle” or trying to retool your skill set or career.
Data science is a broad discipline with a few accredited cetification programs. However, many of those programs are cost-prohibitive.
“There are at least 50 Data Science certification programs by universities worldwide offering degree and diplomas in this discipline,”writes data science blogger, Zeeshan Usman. “It costs from $50,000 to $270,000 and takes one to four years of your life.”
And although somewhat new in the nomenclature, data science encompasses many skills that professionals may already have acquired through work or educational experience such as:
Data scientists also need to have an understanding of and exposure to reproducibility, decision-making, and working with stakeholders and executives. So attaining a second (or third) degree may not be the best option for professionals looking to break into data science. Yet to be successful, they still need to communicate their experience, skills, and acquired knowledge to prospective employers.
Put yourself in the position of a hiring manager; say her name is Paula. Paula has a couple of full-time openings for a Data Scientist. She has done a lot of research and has decided to hire at an entry-level. The person she hires will need to work with her and her staff of data scientists, business analysts, data analysts, and business intelligence developers.
She has a stack of resumes to read through and she has several questions that need to be answered before she decides to set up phone interviews. However, Paula knows that curriculum vitae (CV) often do not align with the skills and experience needed for a Data Scientist position. She may see many CVs from college graduates, she may see many resumes from men and women with several years of work experience but without any practical data science experience. But since this is an entry-level position, she will likely not see any applicants with real knowledge in the field of data science.
So what should Paula focus on? How should she shortlist applicants for the phone interview? How can Paula know which of the applicants has a real, proven commitment to becoming a Data Scientist? She may very well focus on the resumes that list Data Science certifications along with any pertinent business experience! And if those certification programs have compulsory projects to complete, that’s even better. You can begin to build your portfolio of data science projects before ever landing a job.
Data science is a fast-growing field. According to an article by Forbes, IBM predicts the demand for data scientists will grow by over 25 percent by 2020. Budding Data Scientists need to get their resumes and CVs out there as soon as is practicable, but they still need to gain valuable experience with those data science skills mentioned above. Data Science certifications are the quickest way to learn and hone the skills and techniques necessary to land that first data science job.
Furthermore, Data Science certifications allow students to learn and hone skills that won’t normally be acquired through work experiences, such as exploratory analysis skills, visualization skills, and data mining/machine learning algorithms.
So get your certifications in R, Python, and SQL. Or learn Hadoop or Apache Spark. Take some statistics courses. Practice everything you learn, every day.
These Data Science courses co-developed with IBM will give you an insight into Data Science tools and methodologies, which is enough to prepare you to excel in your next role as a Data Scientist. You will earn an industry-recognized certificate from IBM and Simplilearn that will attest to your new skills and on-the-job expertise. This Data Science certification training will make you learn R, Python, Machine Learning techniques, data reprocessing, regression, clustering, data analytics with SAS, data visualization with Tableau, and an overview of the Hadoop ecosystem.