Your Shopping Cart
Enroll now to become a Certified Data Science expert with CertZip Data Science Masters Program and upgrade your skills.
Data Science Masters Program makes you experienced in tools and systems utilized by Data Science Professionals. It contains training in Statistics, Data Science, Python, Apache Spark & Scala, Tensorflow, and Tableau.
Data Science Masters Program makes you skilled in tools and systems operated by Data Science Professionals. It possesses training in Statistics, Data Science, Python, Apache Spark & Scala, Tensorflow, and Tableau.
A data science master's program helps you acquire skills to collect, manage and analyze data, its types, trends, and deliver the results accordingly. This advanced skill set is spread out throughout the M. Sc.
There are no prerequisites for enrollment in the Data Science Masters Program.
experienced professional working in the IT industry, an aspirant preparing to join the world of Data Science
Today, companies across multiple industries operate and rely on data scientists to develop their businesses. Data scientists are generally responsible for collecting and analyzing raw data, using data to gain insights into business processes to help achieve various goals.
Data scientists study which queries need responding to and where to locate the related data. They have business acumen and analytical skills and can mine, clean, and present data. Businesses utilize data scientists to source, manage, and analyze large amounts of unstructured data.
As companies hope to extract essential insights from big data, the demand for data scientists is on a consistent rise. Reports suggest that India is the second-highest country after the US created the requirement to recruit around 50,000 data scientists in 2020 and 2021.
Data scientists examine which questions need answering and where to discover the corresponding data. They have business acumen and analytical skills and can mine, clean, and present data. Businesses use data scientists to source, manage, and analyze large amounts of unstructured data.
Understand data and its types accordingly sample data and derive meaningful information from the data in different statistical parameters.
learn about probability, interpret & solve real-life problems using chance. You will get to know the power of possibility with Bayesian Inference.
Illustrate inferences from present data and construct predictive models utilizing various inferential parameters (as a constraint)
understand the various methods of testing the alternative hypothesis.
introduction to Clustering as part of this Module which forms the basis for machine learning.
Discover the roots of Regression Modelling operating statistics.
learn data and its types and will accordingly sample data and derive meaningful information from the data in terms of different statistical parameters
learn about probability, interpret & solve real-life problems using chance. You will get to know the power of possibility with Bayesian Inference.
learn to draw inferences from present data and construct predictive models using different inferential parameters (as the constraint).
understand the various methods of testing the alternative hypothesis.
get an introduction to Clustering, which forms the basis for machine learning.
know about the roots of Regression Modelling operating statistics.
introduction to Data Science and see how Data Science helps to analyze large and unstructured data with various tools.
know about various statistical techniques and terminologies utilized in data analysis.
Discuss the various sources available to extract data, organize the data in a structured form, examine the data, and represent the data in a graphical format.
introduction to Machine Learning, discuss the different categories of Machine Learning and execute Supervised Learning Algorithms.
learn the Supervised Learning Techniques and implement various techniques, such as Decision Trees, Random Forest Classifier, etc.
Know about Unsupervised Learning and the different types of Clustering that can be utilized to analyze the data.
know about association rules and various types of Recommender Engines.
Examine Unsupervised Machine Learning Techniques and the execution of various algorithms, for example, TF-IDF and Cosine Similarity.
learn about Time Series data, different components of Time Series data, Time Series modeling - Exponential Smoothing models, and ARIMA model for Time Series Forecasting.
Intro to Reinforcement Learning and Deep Learning concepts, discuss Artificial Neural networks, the building blocks for Artificial Neural Networks, and a few Artificial Neural Network terminologies.
