Data Analytics Course in Hyderabad
Soar like a juggernaut in your career with our " Data Analytics using Python and R programming" course. Perform Exploratory Data Analysis and Data Mining on Big Data with the twin engines of Python and R. Master Machine Learning, Neural Networks, Descriptive/ Predictive Modelling with Python and R for better business decision making. Cement a firm foundation in the sphere of Big Data Analytics with our data analytics course. Enroll in 360DigiTMG - the best data analytics institute in Hyderabad.
On-campus training: 48 hours
Data Analytics Training in Hyderabad
This data analytics course lays the foundation of Big Data analytics and Machine Learning with Python and R. Initially, students are exposed to Data Preparation, Data Cleansing, Exploratory Data Analysis, Feature Engineering, Feature Extraction, and Feature selection. The use of advanced regression analysis to enable predictive modelling and hypothesis testing is explained. Data Mining techniques both Supervised and Unsupervised are explored in great depth. Scripting Machine Learning algorithms and Neural Networks in Python and R is the key highlight of this course. Create compelling graphics with the data visualization software Tableau.
Data Analytics Training Outcomes in Hyderabad
Data Analytics Course Modules in Hyderabad
Data Science is the sexiest job of the 21st century. Learn about the applications of Data Science in various sectors and industries. Organizations need to analyze the humongous amount of data collected from various touchpoints, to stay ahead of the competition. In this module, you will comprehend the benefits of employing Data Science in different industries and sectors with the help of cogent use cases.
Data Science projects are implemented in stages like, business and data understanding, data preparation and modeling, model evaluation, and finally deploying the solution. These steps are part of the Project Management methodology called CRISP-DM. In this module, you will understand the different stages of the Project Management framework CRISP-DM.
In this module, you will learn about the various steps involved in Data Preparation. The data collected by organizations is usually in a raw format that cannot be directly used for model development. It has to be processed with Data Preparation and Data Cleansing. Learn the various statistical computing and visualization techniques to apply Descriptive Analytics.
Data is key while performing analytics for organizations to derive meaningful insights. Data stored in various data sources need to be extracted and cleaned before it can be consumed. Features that are relevant to solve a business problem, need to be identified using various techniques. In this module, you will learn to extract features by selecting the relevant ones.
Learn the art of decision making using Inferential Statistics. Understand the business problem and devise the Hypothesis Testing. Understand the different Hypothesis Testing paradigms used in different scenarios. Also, learn to use the appropriate test based on the data being analyzed. In this module, you will also learn about the most popular numeric value Prediction Model - the Regression Model. Understand the mathematical explanation for Linear Regression Models.
The prediction of a numeric dependent variable is performed by Regression Methods. Learn the variations in Regression Models used for continuous and discrete numeric values, learn about their mathematical applications based on the distribution of the Dependent Variable. Understand the Discrete Probability Distributions along with Continuous Probability Distribution and Regression Methods used for the prediction of these variables.
Data Mining is a quantitative technique used to churn data. Unsupervised Learning techniques are used to derive hidden patterns from data. All the attributes in the data are treated as independent variables. Hence in these techniques, the prediction of any specific variable does not occur. Data Segregation, and identifying the relations between entities (rows and columns) is enabled.
A Computer will understand unstructured text data, only if you first bring a structure to the data. In this module, you will learn how to extract features from unstructured data using a Bag Of Words Technique. This is used to create a key-value paired data called DTM or TDM. Learn to interpret the sentiment of keywords extracted, from review data. Advanced concepts of machine learning used to extract context/sentiment and emotions from keywords are discussed as part of Natural Language Processing.
Data Mining Supervised Learning techniques are also called Machine Learning Algorithms. As part of this module, you will learn the mathematically explainable, Prediction And Classification Models. Techniques are broadly segregated as Regression And Classification Techniques, based on the dependent data type. You will learn rule-based classification models like a decision tree and ensemble methods boosting, bagging and random forest. You will also learn the Lazy Learner k-NN method as part of this module.
Data Mining Supervised Learning Black Box techniques which are the go-to techniques used for deep learning are discussed as part of this module. Learn about Neural Networks which are derived from the biological brain model. Understand all the parameters of the Neural Network model and learn the art of tuning these parameters to improve the model. Types of Neural Networks, ANN, CNN and RNN, used in Deep Learning to solve problems on unstructured data. Support Vector Machines are also a Black Box Technique used in Deep Learning for classification and prediction of attributes in a non-linear multidimensional space using boundaries derived by kernel parameters. Learn about the application of these models in real-time use cases.
Attributes affected by time are analyzed using specialized techniques. Learn to interpret the components of a Time Series data, understand how time-series data is different from the cross-sectional data which is collected at a single point of time. Learn about the systematic components level, trend, and seasonality of time series data. EDA techniques to identify these components and predicting these components using forecasting techniques are also part of this module. You will learn the different approaches of forecasting as part of this module.
The demand for new age tech professional in 2020 will be 4.4 Lakhs against a supply of 2.4 Lakhs.(Source: Team Lease)
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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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Data Analytics Panel of Coaches in Hyderabad
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, Ebreeze, 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, AgilePM (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 professionals 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, R, RStudio, Python, Tableau, Cognos.
- Corporate clients include DuPont, All-Scripts, Girnarsoft (College-dekho, Car-dekho) 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 from 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
This Data Analytics course completion certificate demonstrates your prowess in Data Analytics, Machine learning and data visualization. It is proof of your diligence and dedicated endeavours. Use it to distinguish yourself with peers and superiors. Propel yourself in your career path with this certificate from the best data analytics training institute in Hyderabad.
FAQs for Data Analytics Course in Hyderabad
The key objectives of a good data analytics course are:
- Become proficient with different data generation sources
- Master Text Mining to generate Customer Sentiment Analysis
- Analyze and transform Structured and Unstructured data using different tools and techniques
- Learn the techniques of Descriptive and Predictive Analytics
- Apply Machine Learning approached for business decisions
- Build prediction models for day-to-day applicability
- Perform forecasting to take proactive business decisions
- Represent business results using data visualization techniques
The curriculum of this course includes the following subjects:
- Data Preparation
- Data Cleansing
- Exploratory Data Analysis
- Feature Engineering
- Feature Extraction
- Feature Selection
- Hypthesis Testing
- Regression Analysis
- Predictive Modelling
- Data Mining Supervised
- Data Mining Unsupervised
- Text Mining
- Natural Language Processing
- Machine Learning
- Black Box Techniques - Neural Networks, SVM
- Time Series Analysis / Forecasting
- Project Management
The most obvious benefit of pursuing a data analytics course is that one can apply to a plethora of job opportunities available in the data science market. The demand for data science professionals in India it has increased by 400% and the supply has increased by 19% only. This is the most sought after qualification. It is also the most lucrative career option with salaries hitting the ceiling.
Another benefit would be the range of analytical and problem-solving skills that a student acquires from a data analytics course. These skills can be used to analyse big data and draw meaningful insights from the same.
The third benefit is that you exhibit better business decision-making skills in the workplace.
Data Analytics is useful for Chartered Accountants. Chartered accountants can use big data analytics and machine learning to re-engineer the audit process. Network Analytics and Graph Data are used to identify fraudulent practices. The tools of data analytics can be used to detect business risks as well.
360DigiTMG offers a Data Science Course for Internal Auditors and a Certification Program in Financial Analytics. Chartered Accountants can pursue either one of these courses to develop cutting-edge analytical skills.
Data Analytics is widely used in the Financial Services industry today. Finance professionals can benefit from a data analytics course. They will understand how data analytics is employed in Stock Market Investments, Banking, Financial Advisory and Management, EPS etc. The application of Artificial Intelligence in Algorithmic Stock Trading, Automated Robo- Advisors and Fraud Detection Systems are also elaborated in-depth in a financial analytics course.
360DigiTMG offers a very comprehensive Certification Program in HR Analytics for HR professionals. The module includes
- Enabling Workforce Analytics
- Predictive Modelling for Ethnic Diversity
- Machine Learning to predict Employee Turnover
- NLP techniques to screen and recruit candidates
- Predicting Employee Performance
- Predictive Modelling of sickness/ absence
- Deep Learning for Emotion Mining in Workforce Analytics
360DigiTMG provides a good Certification Program on Life Sciences and Healthcare Analytics meant for medical professionals. The course is devoted to Clinical Healthcare Data Analysis. Medical professionals will learn to interpret Electronic Health Record (EHR) data types and structures and apply predictive modelling on the same. In addition to this, they will learn to apply machine learning techniques to healthcare data.
Students must possess a Bachelor's degree in Mathematics/ Statistics/ Computer Science / Data Science or a Bachelor's degree in Engineering( any discipline) from a recognized institute.
The duration of this course is three months. It comprises of 48 hours of classroom sessions, 80 plus hours of assignments and e-learning and 80 plus hours of live project work.
The course material can be downloaded from our online Learning Management System (AISPRY).
We do teach data visualization with Tableau as part of the data analytics course.
As soon as a student joins a course he is assigned a mentor. If the institute feels that a particular student requires additional assistance then they will assign some more mentors for a single student.
We host several free webinars on data analytics on youtube. They can be accessed from the link given belowhttps://www.youtube.com/channel/UCNGIDQ466bNY87eEeKeQuzA
No. The cost of the certificate is absorbed in the course fee.
All classroom sessions are video recorded and lodged in our Learning Management System AISPRY. If you miss a data analytics classroom session you can access the recorded session from the Learning Management System.
Once a student completes his course and receives the Course Completion Certificate, he is eligible for an internship. We offer an internship with INNODATATICS ltd. The students gets involved in a live project with INNODATATICS. At the end of his internship, he will receive an Internship Certificate in recognition of his efforts.
A fresh graduate will greatly benefit from the internship opportunity with INNODATATICS that our institute offers. He will work on a live project and get hands-on experience of implementing a data analytics solution. This will improve his employability immensely. Most employers value live project experience only.
You can apply for the following jobs after completing the course:
- Data Analyst
- Data Scientist
- Data Engineer
- Data Architect
- Business Analyst
A Data Analyst deals with Data Cleansing, Exploratory Data Analysis and Data Visualisation, among other functions. The analyst's job is to sift through historical data to understand the present state of the business.
A Data Scientist builds algorithms to solve business problems using statistical tools such as Python, R, SAS, STATA, Matlab, Minitab, KNIME, Weka etc. He also performs predictive modelling to facilitate proactive decision-making. Machine learning algorithms are used to build predictive models using Regression Analysis and a Data Scientist must develop expertise in Neural Networks and Feature Engineering.
A Data Engineer is essentially a programmer in Spark, Python and R and complements the role of a data scientist.
A Data Architect is entrusted with the task of establishing hardware and software infrastructure needed to perform Data Analysis. They have to select Hard Disk, Network Architecture, Databases, GPUs etc.
We offer end to end placement assistance to our students. We assist them in resume preparation and conduct several rounds of mock interviews. We circulate their resumes to reputed placement consultants with whom we have a long-standing agreement. Once placed we offer technical assistance for the first project on the job.
Business Analytics is the emerging field in data science. It is definitely worth pursuing a data analytics course after your MBA. You can specialize in Financial Analytics, HR Analytics or Supply Chain Analytics. Once you finish your data analytics course you can apply for the position of Business Analyst.
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.