Data Science Course Training in Pune
Fast-track your career with the Certification Programme in Data Science. Master all the key tools and techniques in Data Science and pick up domain-specific skills to add more value to your profile.
On-campus training: 120 hours
Data Science Training in Pune
The Data Scientist Certification Programme is one of the most comprehensive Data Scientist courses in Pune. It is specially designed to suit both data professionals and beginners who want to make a career in this fast-growing profession. Over 3 months, students will learn key techniques such as Statistical Analysis, Regression Analysis, Data Mining, Machine Learning, Forecasting and Text Mining, and tools such as Python and R Programming.
Data Science Training Learning Outcomes
Data Science Certification Modules
Project Management insights need to be learnt for the implementation of any analytics projects. Cross-Industry Process for Data Mining (CRISP-DM) methodologies are used for Data Analytics projects which are broadly explained in 6 stages. You will be introduced to the tasks performed in these 6 stages to successfully develop and deploy an Analytics solution.
Understand the business problem and map the problem objectives with the Data provided to derive insights. Learn to perform Descriptive Analytics and understand the concepts of Data Preparation, Data Cleansing, Feature Engineering, Imputation, etc. as a part of this module.
Learn to draw insights by applying statistical calculations on the Data. Business moments calculations will yield information on the raw data. Understand about these business moments calculations and the insights they derive. Learn how Descriptive Analytics can be better performed by visualizing the details for storytelling.
In this tutorial, learn to intercept the details each of the plots explain about the Data. Understand the Pros & Cons of each technique and learn to choose the appropriate technique to be used in different scenarios. Learn how to plot using functions of Python and R. Understand the art of estimation and inference answers with confidence for business problems based on the small data obtained by sampling on population. Learn about the difference between Parameters and Statistics and understand the process of Inferential Statistics.
The chance of the estimated value is called probability, in this tutorial, you will revise the basic mathematical concepts of probability and its calculations. Understand to intercept the spread of the probability to estimate value with confidence. A Probability Distribution is a pattern in the Data and you will learn to interpret the distribution of the Data using examples.
Hypotheses testing is the process of making assumptions and testing the same for a business problem. In this module, learn the rules to make assumptions and understand the flow of performing the tests to evaluate these assumptions in different conditions. Learn the conditions and errors that may arise while performing the hypothesis for a business condition. You will learn to choose the appropriate hypothesis testing based on Data and Business Problems.
Predictive Analytics helps in estimating a value for a condition upfront to assist the businesses to brace for the future. Under the Data Mining Process, Supervised learning concepts are used for Predictions. Learn about the explainable Machine Learning Technique called Regression.
You will learn about the Bi-Variate Analysis using a Scatter Plot and Correlation Analysis to interpret the relationship between variables. Understand the concept of the straight-line equation and its usage for the prediction of a dependent variable.
In this tutorial, you will learn the prerequisites and post-requisites for fitting a linear model for the Data. Understand the challenges in constructing a linear model to regress a dependent variable in a multi-dimensional space. You will learn to deal with Collinearity conditions, Heteroscedasticity conditions. You will also learn how to improve the accuracy of the prediction models.
Understand the Model Evaluation Techniques using Error Function. Learn about the different levels of accuracy levels for the models. In this module, you will learn the conditions of Overfitting and Under-fitting. Understand the regularization techniques L1 and L2 to handle variance and bias by penalizing the coefficients.
In this tutorial, you will learn about the Binary Value Prediction based on a Linear Model. It is the simplest approach for binary classification problems among the Machine learning Algorithms using Maximum Likelihood Estimate (MLE) technique. You will learn how the Logistic Regression will predict the binary outcome by using cutoff value with probability values. Understand the Model Evaluation Technique using Confusion Matrix along with other metrics collected to improve the model.
Classification model for predicting multiple categorical Data which is based on probability calculation similar to logistic regression. In a logistic regression model, you will learn to predict a binary outcome, whereas if the outcome has more than 2 categories then multinomial regression is used. Understand the difference between the types of logistic regression models and learn about multi logit function.
Learn to work with count data using these advanced regression techniques. Linear Models are used in the case of continuous and binary dependent cases, where generalized linear models are applied for positive discrete data value predictions. Learn about discrete data distributions and techniques to predict them. You will learn about Poison and Negative binomial models and learn about the conditions on when to use them.
Clustering is a process of segregating the homogeneous records in the Data. Data Mining unsupervised learning techniques are used to identify the pattern among the raw data collection.
Clustering helps in deriving homogeneity which in-turn helps in applying simple statistical computing to derive meaningful insights. You will learn how Clustering is different from Prediction Techniques. In this tutorial, you will learn about the different approaches to achieve the data segregation for multivariate data.
High Dimensional Data handling is a complex task. Applying any statistical models on high dimensional data is time-consuming and gives low inaccuracy. In this module, learn about how to deal with high dimensional data by capturing information from all the original attributes into a low dimensional space. You will use matrix computation logic to understand how low dimensional data is equivalent to the original data.
Relationship between entities is analysed in this module. The frequently occurring entities are identified to define the dependency between them. Market Basket Analysis technique is a measure of the relationship between entities. Rules are generated based on statistical measures to derive the dependency. You will learn about the drawbacks in the frequency-based approaches and learn how to efficiently define the best association among the entities by considering independence among them.
Unsupervised learning deals with identifying the patterns in the data. As part of this module, you will learn to find the customer behaviour/pattern based on their history. Making the right suggestions to customers will help organizations to retain them. Understand how to define the pattern using distance metrics to make more meaningful suggestions. Learn to measure the similarity between customers using various methodologies. Understand the pros and cons of each technique to derive these patterns.
Learn about measuring a value for nodes/entities in a network. A network could be a social media network or a business network. Understanding the network is essential to organizations to define new revenue generation areas, optimize the current channels of revenues and identify the grey areas in the business network to get a competitive edge.
Learn how to predict a non-numeric dependent variable. k-NN is a simple machine learning algorithm based on Distance Metrics. You will learn the measure of distances based on k value, and also understand the logic of finding the best value of k for classification. A k-NN Algorithm can be used for both predictions of a numeric value and classification of categorical value. Learn about the packages used to implement k-NN classifiers in Python and R.
The graphical representation of data to create classification rules in the form a tree structure is called a Decision Tree. The tree is grown with information content extracted at each branch node from the root node. It is grown till a Decision or label is identified (leaf node). Statistical measure entropy is used to calculate the information content to split the tree into homogeneous branches. Random Forest is a collection of multiple trees to produce an unbiased solution on the business problem
The Decision Tree classification model is a technique which is most prone to overfitting. To improve the reliability and accuracy of the Decision Tree, Ensemble techniques are applied. Bagging which is a parallel approach and Boosting which is a sequential approach are two most popular methods used to handle overfitting problems in Decision Trees.
The Ensemble Techniques try to enhance weak learners by iteratively repeating the training process with low weights assigned to correctly classified data points and high weights assigned to weakly learnt data points, thereby minimizing the overall error. As part of this module, you will learn the Adaboost and Extreme Gradient Boosting techniques developed on complex data.
Majority of the data generated today is in textual format, thanks to social media and the internet made available to smartphones. In this module, you will learn how to handle the unstructured textual data to derive insights. Learn to convert the unstructured data to structured form using the Bag of Words Method. Understand how to read the data from Word Clouds. Advanced concepts of sentiment analysis using natural language processing is also discussed as part of this module.
Revisit the most famous probability algorithm the Bayes Theorem and its applicability in Predictive Analytics as part of this module. How e-mails can be skimmed for the content and classified as spam or ham will be thought of as a use case. Learn how to prepare the input data from text data and apply probability calculations on this data to derive business value.
Learn how a neural network solves complex data problems using the logic of how the biological brain works. Understand the Perceptron Algorithm as part of this module. You will learn how a Perceptron Algorithm learns to solve a linear classification problem. Understand the various parameters used for learning a Perceptron Algorithm. Learn how to deal with non-linear classification problems.
Understand how a network learns based on integration function and activation function. Understand all the hyper-parameters tuned to train the network and update the weights. Learn about weight calculations, learning rates, error functions optimization techniques to reach the least error.
A neural network is the most popular Deep Learning Algorithm used to work with unstructured data. Learn how to handle images, videos using convolution neural networks. Learn about the finer aspects to deal with images with the computer vision OpenCV package. Learn the RNN a variant of a neural network to deal with sequential data like text or voice. Understand how RNN uses learning from the past layer to predict sequential values.
Black Box Technique SVM is a Deep Learning Algorithm used to solve numeric and categorical data predictions using boundaries to create linearly separable homogeneous groups. Understand how the non-linear multi-dimensional spaces are dealt with Kernel Algorithms to bring them to linearly separable spaces in a higher-dimensional space.
Learn to predict the time/duration for an event. You will learn about the applications of survival analysis in critical decision-making areas in life science, health care, marketing, customer retention, etc. Understand how to deal with censor data and types of censored data. Learn about kaplan meier survival function.
Learn about the skills to forecast the future based on historical data. Understand the systematic and non-systematic components of a time series data. Learn how to interpret the components using plots on time series data. Understand the steps to handle forecasting projects using CRISP-DM project methodology. In this module, you will learn about the forecasting models which are based on regression equations.
Data-driven forecasting models deal with time-series data which have high volatility. These techniques are applied when the past is not equal to the future. Estimating the pattern in time series that is based on the historical data. Understand different types of Smoothing Techniques. You will also learn about the Seasonality Index which is used to derive the variations among the seasons in the series.
The Indian Data Science Market will be worth 6 million dollars in 2025 and data analytics outsourcing industry in India is worth $25 million.
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Who Should Sign Up?
- IT Engineers
- Data and Analytics Manager
- Business Analysts
- Data Engineers
- Banking and Finance Analysts
- Marketing Managers
- Supply Chain Professionals
- HR Managers
- Math, Science and Commerce Graduates
- Computer Skills
- Basic Mathematical Concepts
- Analytical Mindset
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Python for Data Science Panel of Coaches
Bharani Kumar Depuru
- Areas of expertise: Data Analytics, Digital Transformation, Industrial Revolution 4.0
- Over 14+ years of professional experience
- Trained over 2,500 professionals from eight countries
- Corporate clients include Hewlett Packard Enterprise, Computer Science Corporation, Akamai, IBS Software, Litmus7, Personiv Alshaya, Synchrony Financials, Deloitte
- Professional certifications - PMP, PMI-ACP, PMI-RMP from Project Management Institute, Lean Six Sigma Master Black Belt, Tableau Certified Associate, Certified Scrum Practitioner, (DSDM Atern)
- Alumnus of Indian Institute of Technology, Hyderabad and Indian School of Business
Sharat Chandra Kumar
- Areas of expertise: Data sciences, Machine learning, Business intelligence and Data Visualization
- Trained over 1,500 professional across 12 countries
- Worked as a Data scientist for 14+ years across several industry domains
- Professional certifications: Lean Six Sigma Green and Black Belt, Information Technology Infrastructure Library
- Experienced in Big Data Hadoop, Spark, NoSQL, NewSQL, MongoDB, Python, Tableau, Cognos
- Corporate clients include DuPont, All-Scripts, Girnarsoft (College-, Car-) and many more
- Areas of expertise: Data sciences, Machine learning, Business intelligence and Data Visualization
- Over 20+ years of industry experience in data science and business intelligence
- Trained professionals from Fortune 500 companies and students at prestigious colleges
- Experienced in Cognos, Tableau, Big Data, NoSQL, NewSQL
- Corporate clients include Time Inc., Hewlett Packard Enterprise, Dell, Metric Fox (Champions Group), TCS and many more
Earn a certificate and demonstrate your commitment to the profession. Use it to distinguish yourself in the job market, get recognised at the workplace and boost your confidence. The Data Science Certificate is your passport to an accelerated career path.
Python & R for Data Science FAQs
While there are a number of roles pertaining to Data Professionals, most of the responsibilities overlap. However, the following are some basic job descriptions for each of these roles.
As a Data Analyst, you will be dealing with Data Cleansing, Exploratory Data Analysis and Data Visualisation
As a Data Scientist, you will be building algorithms to solve business problems using statistical tools such as Python, R, SAS, STATA, Matlab, Minitab, KNIME, Weka etc. A Data Scientist also performs predictive modelling to facilitate proactive decision-making.
A Data Engineer primarily does programming using Spark, Python, R etc. It often compliments the role of a Data Scientist.
A Data Architect has a much broader role that involves establishing the hardware and software infrastructure needed for an organisation to perform Data Analysis. They help in selecting the right database, servers, network architecture, GPUs, cores, memory, hard disk etc.
Different organisations use different terms for data professionals. You will sometimes find these terms being used interchangeably. Though there are no hard rules that distinguish one from another, you should get the role descriptions clarified before you join an organisation.
With growing demand, there is a scarcity of Data Science Professionals in the market. If you can demonstrate strong knowledge of Data Science concepts and algorithms, then there is a high chance for you to be able to make a career in this profession.
360DigiTMG provides internship opportunities through Innodatatics, our USA-based consulting partner, for deserving participants to help them gain real-life experience. This greatly helps students to bridge the gap between theory and practical.
There are plenty of jobs available for data professionals. Once you complete the training, assignments and the live projects, we will send your resume to the organisations with whom we have formal agreements on job placements.
We also conduct webinars to help you with your resume and job interviews. We cover all aspects of post-training activities that are required to get a successful placement.
After you have completed the classroom sessions, you will receive assignments through the online Learning Management System that you can access at your convenience. You will need to complete the assignments in order to obtain your data scientist certificate.
In this blended programme, you will be attending 120 hours of classroom sessions of 3 months. After completion, you will have access to the online Learning Management System for another three months for recorded videos and assignments. The total duration of assignments to be completed online is 60-80 hours. Besides this, you will be working on a live project for a month.
If you miss a class, we will arrange for a recording of the session. You can then access it through the online Learning Management System.
We assign mentors to each student in this programme. Additionally, during the mentorship session, if the mentor feels that you require additional assistance, you may be referred to another mentor or trainer.
No, the cost of the certificate is included in the programme package.
Sharvin Rao8 months ago
Very good exposure. Satisfied with this program. Teaching materials are complete and does not require any programming background to learn this course
Priya Gopal9 months ago
Very experienced trainer and have patience to deal with every query raised in the classroom.
Lavaniya Rajesveran9 months ago
Great place to learn about Data Science . Trainers are knowlegable and shared lots of new terms which was easily understandable.