The Post Graduate Program in Advanced Data Analytics is designed to help you gain the exact set-skills desired by most of the world's leading employers of Data Analysts. Our program offers Capstone Assignments, genuine real-world projects, relevant case studies, and assistance from industry experts who care to help you become an outstanding Data Analyst.
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This Advanced PG program prepares learners to be job-ready in Advanced PG Program in Data Analytics in just 9 months, with a wide range of real- world projects. Skilled Professors, Latest Technologies Included, Outstanding Course Policy, Affordable Payment System, Real-life presentations, & Placement Assured is our major priority.
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Dr. Deepika Sharma has been associated with academics /corporate education for more than 14 years. She has a deep passion in the field of Artificial Intelligence, Data Science, and Machine Learning.
Shankar is a Data Scientist with 14 Years of Experience. His current employment is with Accenture and has experience in telecom, healthcare, finance and banking products.
Experienced Data Scientist with a demonstrated history of working in the information technology and services industry.
Polong Lin is a Data Scientist at IBM in Canada. Under the Emerging Technologies division, Polong is responsible for educating the next generation of data scientists through BDU.
Currently, he is driving several productivity programs - using data analytics to drive insights from business operations and implementing optimizations such as streamlining workflows, improving service levels, and ultimately reducing cost.
Mahdi Noorian is a Postdoctoral Fellow at the Laboratory for Systems, Software and Semantics (LS3) of the Ryerson University. He holds a Ph.D degree in Computer Science from University of New Brunswick.
Best-in-class content by leading faculty and industry leaders in the form of live sessions, pre-recorded HD videos, projects, case studies, industry webinars, and assignments
The Foundations bundle comprises 2 courses where you will learn to tackle Statistics and Coding head-on. These 2 courses create a strong base for us to go through the rest of the tour with ease.
This course will introduce you to the world of Python programming language that is widely used in Artificial Intelligence and Machine Learning. We will start with basic ideas before going on to the language's important vocabulary as search phrases, syntax, or sentence building. This course will take you from the basic principles of AI and ML to the crucial ideas with Python, among the most widely used and effective programming languages in the present market. In simple terms, Python is like the English language.
Python BasicsPython is a popular high-level programming language with a simple, easy-to-understand syntax that focuses on readability. This module will guide you through the whole foundations of Python programming, culminating in the execution of your 1st Python program.
Anaconda Installation - Jupyter notebook operationUsing Jupyter Notebook, you will learn how to use Python for Artificial Intelligence and Machine Learning. We can create and share documents with narrative prose, visualizations, mathematics, and live code using this open-source online tool.
Python functions, packages and other modulesFor code reusability and software modularity, functions & packages are used. In this module, you will learn how you can comprehend and use Python functions and packages for AI.
NumPy, Pandas, Visualization toolsIn this module, you will learn how to use Pandas, Matplotlib, NumPy, and Seaborn to explore data sets. These are the most frequently used Python libraries. You'll also find out how to present tons of your data in simple graphs with Python libraries as Seaborn and Matplotlib.
Working with various data structures in Python, Pandas, NumpyUnderstanding Data Structures is among the core components in Data Science. Additionally, data structure assists AI and ML in voice & image processing. In this module, you will learn about data structures such as Data Frames, Tuples, Lists, and arrays, & precisely how to implement them in Python.
In this module, you will learn about the words and ideas that are important to Exploratory Data Analysis and Machine Learning. You will study a specific set of tools required to assess and extract meaningful insights from data, from a simple average to the advanced process of finding statistical evidence to support or even reject wild guesses & hypotheses.
Descriptive StatisticsDescriptive Statistics is the study of data analysis that involves describing and summarising different data sets. It can be any sample of a world's production or the salaries of employees. This module will teach you how to use Python to learn Descriptive Statistics for Machine Learning.
Inferential StatisticsIn this module, you will use Python to study the core ideas of using data for estimating and evaluating hypotheses. You will also learn how you can get the insight of a large population or employees of any company which can't be achieved manually.
Probability & Conditional ProbabilityProbability is a quantitative tool for examining unpredictability, as the possibility of an event occurring in a random occurrence. The probability of an event occurring because of the occurrence of several other occurrences is recognized as conditional probability. You will learn Probability and Conditional Probability in Python for Machine Learning in this module.
Hypothesis TestingWith this module, you will learn how to use Python for Hypothesis Testing in Machine Learning. In Applied Statistics, hypothesis testing is among the crucial steps for conducting experiments based on the observed data.
Machine Learning is a part of artificial intelligence that allows software programs to boost their prediction accuracy without simply being expressly designed to do so. You will learn all the Machine Learning methods from fundamental to advanced, and the most frequently used Classical ML algorithms that fall into all of the categories.
With this module, you will learn supervised machine learning algorithms, the way they operate, and what applications they can be used for - Classification and Regression.
Linear Regression - Simple, Multiple regressionLinear Regression is one of the most popular Machine Learning algorithms for predictive studies, leading to the very best benefits. It is an algorithm that assumes the dependent and independent variables have a linear connection.
Logistic regressionLogistic Regression is one of the most popular machine learning algorithms. It is a fundamental classification technique that uses independent variables to predict binary data like 0 or 1, positive or negative , true or false, etc. In this module, you will learn all of the Logistic Regression concepts that are used in Machine Learning.
K-NN classificationk-Nearest Neighbours (Knn) is another widely used Classification algorithm, it is a basic machine learning algorithm for addressing regression and classification problems. With this module, you will learn how to use this algorithm. You will also understand the reason why it is known as the Lazy algorithm. Interesting Right?
Support vector machinesSupport Vector Machine (SVM) is another important machine learning technique for regression and classification problems. In this module, you will learn how to apply the algorithm into practice and understand several ways of classifying the data.
We explore beyond the limits of supervised standalone models in this Machine Learning online course and then discover a number of ways to address them, for example Ensemble approaches.
Decision TreesThe Decision Tree algorithm is an important part of the supervised learning algorithms family. The decision tree approach can be used to resolve regression and classification problems unlike others. By learning simple decision rules inferred from previous data, the goal of using a Decision Tree is constructing a training type that will be used to predict the class or value of the target varying.
Random ForestsRandom Forest is a common supervised learning technique. It consists of multiple decision trees on the different subsets of the initial dataset. The average is then calculated to enhance the dataset's prediction accuracy.
Bagging and BoostingWhen the aim is to decrease the variance of a decision tree classifier, bagging is implemented. The average of all predictions from several trees is used, that is a lot more dependable than a single decision tree classifier.
Boosting is a technique for generating a set of predictions. Learners are taught gradually in this technique, with early learners fitting basic models to the data and consequently analyzing the data for errors.
In this module, you will study what Unsupervised Learning algorithms are, how they operate, and what applications they can be used for - Clustering and Dimensionality Reduction, and so on.
K-means clusteringIn Machine Learning or even Data Science, K-means clustering is a common unsupervised learning method for managing clustering problems. In this module, you will learn how the algorithm works and how you can use it.
Hierarchical clusteringHierarchical Clustering is a machine learning algorithm for creating a bunch hierarchy or tree-like structure. It is used to group a set of unlabeled datasets into a bunch in a hierarchical framework. This module will help you to use this technique.
Principal Component AnalysisPCA is a Dimensional Reduction technique for reducing a model's complexity, like reducing the number of input variables in a predictive model to avoid overfitting. Dimension Reduction PCA is also a well-known ML approach in Python, and this module will cover all that you need to know about this.
DBSCANDensity-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to identify arbitrary-shaped clusters and clusters with sound. You will learn how this algorithm will help us to identify odd ones out from the group.
Tableau is a visual platform for business intelligence and analytics which allows users to monitor, comprehend, observe, and make choices with a range of data. It allows you to produce any kind of graph, plot, or chart without scripting.
Data handling & summariesYou can learn how to handle various types of data, plot, analyze, sort, and summarise the entire data.
Building Advanced Reports/ MapsTableau Reports are a built-in feature of Tableau that allows users to display data from different sources into reports to better comprehend how far they have progressed toward their goals, better understand their customers' needs, and forecast future plans.
Calculated FieldsYou will use calculated fields to generate new data from existing data in your data source. When you build a calculated field in your data source, you are efficient in including a new field (or column) whose values or members are determined by a computation that you control.
Table calculationsTable computations are modifications that can be applied to the data in a display. They are a kind of calculated field that works with Tableau's local data based on what is already in view.
ParametersParameters let you change a reference line, band, or box dynamically. For instance, reference a parameter rather than a reference line at a defined position on the axis. The reference line could be moved using parameter control.
Building Interactive DashboardsAn interactive dashboard allows you to dive down and filter operational data, allowing you to see data from multiple angles or in more depth. Dashboards allow data-driven business choices by providing a simplified and clear overview of overall business information.
Building StoriesA narrative in Tableau is a set of visuals that work together to present data. You can use tales to convey a data story, offer context, display how steps affect results or make a strong argument.
Working with DataIn this module, you will learn to join the tables, data blending and make connections, etc.
Sharing work with othersIn this module, you will learn to share Workbooks, Publish to Reader/PDF and Publish to Tableau Server and share on the web.
Power BI is an analytics tool that enables business users to analyze data and distribute insights throughout the organization at all levels. Through interactive and easy dashboards, Power BI offers an end-to-end view of important metrics and key performance indicators, all in real-time and in one place.
Get Started Building with Power BIIn this module, you will learn how to find Power BI services and apps that interact. Investigate how Power BI can help you to run your business more effectively, and figure out how to make eye-catching graphics and reports.
Get Data In Power BIYou will use Power BI to connect to data in text-based files, third-party directories, Microsoft Azure directories, along with web services like Google Analytics and SalesForce, among others. You will learn about how you can get the data in Power BI.
Clean, Transform and Load data in Power BIPower Query offers a series of capabilities designed to help you in cleaning and preparing your data for analysis. For deeper analytics, you will learn ways to simplify a complicated model, alter data types, and rename items.
Design a Data Model In Power BIIt is about simplifying the chaos with regard to creating an outstanding data model. In this module, you will find out about the vocabulary and implementation of star schemas, which is one technique to simplify a data type. You will also learn why selecting the right data granularity is vital for Power BI report efficiency and readability.
Visualization in Power BIVisualizations are representations of data insights. A Power BI report may have a single page with a single graphic or a lot of pages with multiple visuals. Visuals from reports can be connected to dashboards in the Power BI service.
Introduction to Creating Measures using DAX in Power BIData Analysis Expressions (DAX) is a scripting language that is used to create custom tables, measurements, or calculated columns in Microsoft Power BI. It is a set of capabilities, operators, and also constants that can be used to calculate and return one or more values using a formula, or expression.
Create dashboards in Power BIDashboards enable report users to make a single object of directed data that is created for particular requirements. Dashboards could be made up of different graphics pulled from various reports.
On Premises Data Gateway ManagementEach service combines gateways differently and administration choices can change. This module pertains to the manage gateways page in Power BI, also, you will deal with gateways from any service.
Implement Row-Level SecurityWith Power BI, Row-Level Security (RLS) can be used to restrict data access for users. You can add filters inside roles to limit data access at the row level. In this module, you will learn how to set up RLS.
Work with AI Visuals in Power BIWith Power BI, row-level security (RLS) can be used to limit data access for users. You can build filters inside roles to restrict data access at the row level. With this module, you will learn how to set up RLS.
Create Paginated ReportsPaginated reports, as the name implies, can span many pages. They are formatted in a certain way and allow for exact modification. They are used to create paginated reports. The Power BI Report Server web portal, like the SQL Server Reporting Services (SSRS) website, allows you to save and control paginated reports.
Case StudyYou will also get a chance to focus on multiple use cases that will enable you to improve your analytical.
The curriculum has been designed by faculty from IITs, and Expert Industry Professionals.
Hours of Content
Live Sessions
Tools and Software
Live Assesments based on real-life scenerio and many Data Analytics project like data cleaning, web scraping, and many fresher-friendly projects, etc.
The crude accelerometer and whirligig sensor information is gathered from the cell phone and smartwatch at a pace of 20Hz.
In the connected world, it is imperative that the organizations are using to Recommend their Products & Services to the People.
Based on The Data Collected from the Meteorological Department, Predicting The Air Quality Of Different Parts of The country
DataTrained offers an Advanced PG program in data analytics that involves professional mentors, the updated tools and techniques, the ideal payment system and also with real-time project experience included, and 100% job assurance, all at an attractive cost, making it the best advanced data analytics PG program ever.
Partnered with IIMJobs wherein you get access to their paid resume preparation kit and personal feedback from the industry HR experts. An individual career profile is prepared by our experts so that it suits his/her experience and makes it relevant to a Data Scientist role.
Regular mock HR and Technical interviews by mentors with personal guidance and support. The industry mentor helps learners to take projects on Kaggle and move on to the status bar so that their resume looks competitive to the recruiters.
We generate the Ability Score of every individual which is then sent to our more than 950+ recruitment partner organizations. At last, we organize campus placements quarterly in Noida, Gurgaon, Ahmedabad, Bangalore, and Chennai to place our students.
DataTrained is offering the best online Advanced Data Analytics in India. With 10,000+ careers transformed.
DataTrained has helped me with the vital knowledge and skills that are needed for a data scientist role. The trainer starts with an example to make us comprehend the concept and then help us build the Algorithms with the real industry datasets. DataTrained brings the power of online learning along with dedicated Mentorship, Counselling, Live Sessions and 6 months Internship.
I saw an ad from DataTrained on facebook and I contacted them straight away and enquired about their Data Science online course. Their counselor took me through the complete journey of what they offer and what is data science all about. After continuous conversation for a few weeks, I was pretty sure about the course and now I knew where I need to invest my money and hard work.
The program is a well-balanced mix of pre-recorded classes, live sessions on weekends and printed reading materials they sent to my address. My mentor was Amit Kaushik and he helped me in getting that confidence and completing my assignments on time.I have almost completed the course and have been able to crack Glenmark interview.Thank you so much DataTrained.
Honeywell
Internshala
Firstsource Solutions
Indium Software
After my graduation, I didn't want to pursue MBA since everyone is doing it I wanted to do something different but I was confused. I opted for the PG Program in Data Science by Data Trained Education and I had an amazing journey with them, the trainers were top-notch, the course content was perfect.
I can certainly say the content they are offering is really good. Assignments are relatable. Completing the assignments helps in a better understanding of the module. In a nutshell, I would recommend this course to anyone interested in Data Science.
HCL
MSMEx
Deqode Solutions
Quantiphi
There are 3 simple steps in the Admission Process that are detailed below
Fill up the Query Form and one of our counselors will call you & understand your eligibility.
Our Admissions Committee will review your profile. Upon qualifying, an Email will be sent to you confirming your admission to the Program.
Block your seat with a payment of INR 10,000 to enroll in the program. Begin with your Prep course and start your Advanced Data Analytics journey!
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