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Best Data Science Online Training in Hyderabad

Course Highlights:<br>Practical Learning: Our hands-on approach allows you to gain practical experience by working on real-world data science projects. You'll learn to extract insights, analyze trends, and make data-driven decisions.<br>Cutting-Edge Curriculum: Stay at the forefront of data science with a curriculum that covers the latest tools and techniques, including data analysis, machine learning, data visualization, and more.<br>Expert Instructors: Learn from seasoned data scientists and industry experts who will guide you through the intricacies of data analysis and modeling, providing valuable

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Best Data Science Online Training in Hyderabad

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  1. About Tsofttech is a IT Training Institute in India, offering vast IT learning courses via class room training, corporate training and online training platform. Expanding its operations across USA, UK, Europe and Asia, Tsofttech Training has acquired huge market in India for IT learning. Students, job seekers and IT professionals come from various backgrounds are always in search of short-term IT programs to get knowledge and expand their skill-set. This has been a constant requirement from IT sector as new software applications keep getting added demanding IT workers to be able to work on these new systems. To circumvent these pitfalls and to enhance work efficiency, Tsofttech Training has chosen few popular IT programs such as Microsoft Dynamics, SAP, Oracle, SAS, ServiceNow and Other ERP tools and many other to offer online IT Training via classroom, online training and corporate training to easily attend the training program in order to get certified and draw the benefit of skill in software. Tsofttech Training not only invites individuals but also small and medium corporate establishments for training their employees tsofttech.com Data Science | 2

  2. About Our In today's data-driven world, organizations are constantly seeking skilled professionals who can harness the power of data and artificial intelligence to drive business growth and innovation. A job-oriented program in Data Science and AI is designed to equip individuals with the knowledge and skills required to excel in these high-demand fields. This program focuses on practical, real-world applications, ensuring graduates are well-prepared for the workforce. Key Components of the Program: Data Science Fundamentals Machine Learning and AI Tools and Technologies Real-World Projects Industry-Relevant Skills Job Placement Assistance A job-oriented program in Data Science and AI is an excellent choice for individuals looking to build a successful career in high-growth, high- impact fields. By gaining hands-on experience, industry knowledge, and practical skills, graduates are well-positioned to meet the demands of the data-centric job market and drive innovation in their chosen industries. tsofttech.com Data Science | 3

  3. Program Highlights Comprehensive Curriculum The program offers a well-rounded curriculum that covers fundamental and advanced topics in data science and artificial intelligence. Topics may include statistics, machine learning, deep learning, natural language processing, computer vision, and more. Hands-On Projects Emphasis on practical, hands-on projects to apply the learned concepts in real-world scenarios. Students work on industry- relevant projects to build a portfolio of work that showcases their skills to potential employers. Guest Lectures and Industry Experts Inviting guest speakers and industry experts to provide insights into the latest trends and real-world applications in data science and AI. Job Placement Support Offering job placement assistance, including resume building, interview preparation, and access to job listings. Establishing connections with potential employers in the field. Online Learning Resources Access to online learning materials, recorded lectures, and resources to facilitate continuous learning even after completing the program. tsofttech.com Data Science | 4

  4. Program Curriculum Introduction to DataScience Introduction to DataScience Discussion on Course Curriculum Introduction to Programming Python – Basics Data types and type conversion Variables Flow Control : If, Elif, Else Data Structures, Indexing, SlicingIf, Else&For Loop & ,While loops Python - Data Types & Utilities List,ListofListsandListComprehension Set and Tuple DictionaryandDictionarycomprehension FunctionsandAnonymousFunctions Map,Reduce,Filter Advance Python Error / Exception Handling File Handling Docstrings Debugging Modules and packages OOPS Installation SQL Basics of DBMS Basics of SQL SELECT WHERE Statements JOINS Python Essential Packages Statistics Basics Numpy Pandas Data Visualization Library : Matplotlib, Seaborn Descriptive Statistics : Central Tendency Variance ,Standard Deviation Covariance Perason's and Spearman Correlation Coefficients Different types of Plots for Continuous, Different types of Distributions Percentiles & quartiles, 5 number summary Statistics Advanced Population and Sample Sampling Distribution and Central Limit Theorem Standard Error Confidence Interval Hypothesistesting : Onetail, Twotail and p-value Z-test, t-test, F-test, chisquare test Visualization using Power BI and Tableau Installations of Power BI and Tableau Basics of Power BI and Tableau Creating Visualization using Power BI and Introduction one, two practical dataset tsofttech.com Data Science | 5

  5. Program Curriculum Exploratory Data Analysis Missing values treatment Outlier detection and treatment Plotting(univariate, bi-variate) Column Standardization Treating Categorical Variable Variable Creation and Transformation Understanding Feature Importance conceptually Machine Learning Fundamentals Types of Machine Learning Methods Classification problem in general Different types of metrics for Classification Curse of dimensionality Feature Transformations Feature Selection Imbalanced Dataset and its effect on Bias Variance Tradeoff Overfitting vs Underfitting vs Normal fitting Supervised Machine Learning Part 1 Linear Regression and It's assumptions L1L2 Regularization Forward and Backward selection methods Logistic Regression k-Nearest Neighbor classifier Naive Bayes classifier Decision Tree Support Vector Machine Supervised Machine Learning Part 2 Ensemble : Bagging Random Forest Regressor and Classifier Ensemble : Boosting Gradient Boosting : AdaBoost XG Boost Gradient Descent technique Creating your own Ensemble Classifier Recommendation Engine Unsupervised Learning Part Basics of Clustering : Clustering Metrics, KMeans Algorithm Density Based Clustering DBSCAN Hierarchical Clustering : Agglomerative Market Basket Analysis Unsupervisedmetrics Principal Component Analysis Singualr Value Decomposition Deep Learning Part 1 Population and Sample Sampling Distribution and Central Limit Theorem Standard Error Confidence Interval Hypothesistesting : Onetail, Twotail and p-value Z-test, t-test, F-test, chisquare test Deep Learning Part 2 Regularization Optmizers Hyper parameter sand tuning of the same tsofttech.com Data Science | 6

  6. Program Curriculum Deep Learning Part 3 Convolution alNeuralNetworks(CNN) Project : Image Classification Different types of CNNarchitectures Basic Natural Language Processing Texts, Tokens Bag of Words Basic text classification based on Bag of Words n-gram : Unigram, Bigram Word vectorizer basics, One Hot Encoding intermediate Natural Language Processing Count Vectorizer Word cloud and gensim TF-IDFVectorizer Word 2VecandGlove Text classification using Word2VecandGlove Deep Learning - Part 4 RecurrentNeuralNetwork(RNN) BackPropagationthroughtime DifferenttypesofRNN:LSTM,GRU BiirectionalRNN Seq2Seqmodel(EncoderDecoder) BERTTransformers Textgenerationandclassificationusin gDeep Generative-AI(Chat-GPT) Time Series and Forecasting Different Components of Time Series Statistical Models of time series forecasting AR Time Series Forecasting using Stats model library Time Series Forecasting using Deep Learning Pyspark Installation and working on basic code Data bricks Hadoop Career Services Resume/Profile Building Mock Interviews Career Mentoring Note: All Concepts Theory with mathematics and practical with python programming language tsofttech.com Data Science | 7

  7. Languages and Tools Covered NLTK Projects Heart Disease Prediction Taxi Fare Prediction E-commerce Customer Segmentation Vaccine Usage Prediction Forecasting Sales of Furniture Products Property Price Prediction Faculty Miss. Gopika Purnima Instructor Working as a Data Scientist IIT, Kharagpur Experience • Overall 4+ years of experience in IT industry / Teaching Profession. • Working as a Data Scientist in an organization in Bangalore. • Working as a Freelance Data Science Trainer in other institute. • Associated as Data Science trainer at T Soft tech, Hyderabad. Education • B.tech - Andhra University • M.tech - Indian Institute of Technology, Kharagpur • Data Science and Machine Learning - IIT Guwahati • Python Programing - IIT Kanpur tsofttech.com Data Science | 8

  8. @tsofttech info@tsofttech.com +91 99485 68787 www.tsofttech.com tsofttech tsoft_technologies tsofttech Scan For more Info Srinivasam, #202, Addaguta, Near JNTU, Kukatpally, Hyderabad- 500072

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