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Call Us:+91-7200228124 Mail Us:info@itraintechnologies.com Data Science with Python Training • CourseFeatures • Learn withProfessionaltrainerswhohave8+yearsofhandonexperienceinthesame domain. • Training will be 20% theory and 80% hands-on with Realexample • Weprovideyouthebesttrainingwithreal-timeprojectsamplesandexamples • Ourtrainingsyllabuscustomisedto helpsfresher andworkingprofessionalstolearnindepth and alsohelpyoutoattendan interviewwithconfidence. • Job Assistance and Placement Support After end of theclass • CourseCertificateprovidedattheendofCourse.
Call Us:+91-7200228124 Mail Us:info@itraintechnologies.com Training Details Course Duration: 45-50hours Batch Available: WEEKDAYS & WEEKENDS Benefits Free Demo Live projects For Practice Case Materials Resume Preparations Interview Guidance and Support 100% Placement & Support
Call Us:+91-7200228124 Mail Us:info@itraintechnologies.com Data Science with Python Training Syllabus • Section 4: Python: Environment Setup and Essentials • Introduction to Anaconda • Installation of Anaconda Python Distribution – For Windows, Mac OS, and Linux • Jupyter Notebook Installation • Jupyter Notebook Introduction • Variable Assignment • Basic Data Types: Integer, Float, String, None, and Boolean; Typecasting • Creating, accessing, and slicing tuples • Creating, accessing, and slicing lists • Creating, viewing, accessing, and modifying dicts • Creating and using operations on sets • Basic Operators: ‘in’, ‘+’, ‘*’ • Functions • Control Flow • Section 1: Data Science Overview • Data Science • Data Scientists • Examples of Data Science • Python for Data Science • Section 2: Data Analytics Overview • Introduction to Data Visualization • Processes in Data Science • Data Wrangling, Data Exploration, and Model Selection • Exploratory Data Analysis or EDA • Data Visualization • Plotting • Hypothesis Building and Testing • Section 3: Statistical Analysis and Business Applications • Introduction to Statistics • Statistical and Non-Statistical Analysis • Data Distribution: Central Tendency, Percentiles, Dispersion • Histogram
Call Us:+91-7200228124 Mail Us:info@itraintechnologies.com Data Science with Python Training Syllabus • Section 7: Data Manipulation with Python • Introduction to Pandas • Data Structures • Series • Data Frame • Missing Values • Data Operations • Data Standardization • Pandas File Read and Write • SQL Operation • Section 8: Machine Learning with Python (Scikit–Learn) • Introduction to Machine Learning • Machine Learning Approach • How Supervised and Unsupervised • Supervised Learning Models • LinearRegression • Supervised Learning Models: Logistic • Regression • K Nearest Neighbors (K-NN) Model • Section 5: Mathematical Computing with Python (NumPy) • NumPy Overview • Properties, Purpose, and Types of ndarray • Class and Attributes of ndarray Object • Basic Operations: Concept and Examples • Accessing Array Elements: Indexing, Slicing, Iteration, Indexing with Boolean Arrays • Copy and Views • Universal Functions (ufunc) • Shape Manipulation • Broadcasting • Linear Algebra • Section 6: Scientific computing with Python (Scipy) • SciPy and its Characteristics • SciPy sub-packages • SciPy sub-packages –Integration • SciPy sub-packages – Optimize • Linear Algebra • SciPy sub-packages – Statistics
Call Us:+91-7200228124 Mail Us:info@itraintechnologies.com • Scikit – Learn Approach Model Training • Scikit – Learn Grid Search and Multiple • Parameters • Pipeline • Section 10: Data Visualization in Python using Matplotli • Introduction to Data Visualization • Python Libraries • Plots • Matplotlib Features: • Line Properties Plot with (x, y) • Controlling Line Patterns and Colors • Section 11: Data Science with Python Web Scraping • Web Scraping • Common Data/Page Formats on The Web • The Parser • Importance of Objects • Understanding the Tree • Searching the Tree • Navigating options • Modifying the Tree • Printing and Formatting • Encoding • Unsupervised Learning Models: • Clustering • Unsupervised Learning Models: • Dimensionality Reduction • Pipeline • Model Persistence • Model Evaluation – Metric Functions • Section 9: Natural Language Processing with Scikit-Learn • NLP Overview • NLP Approach for Text Data • NLP Environment Setup • NLP Sentence analysis • NLP Applications • Major NLP Libraries • Scikit-Learn Approach • Scikit – Learn Approach Built – in Modules • Scikit – Learn Approach Feature • Extraction • Bag of Words • Extraction Considerations
Call Us:+91-7200228124 Mail Us:info@itraintechnologies.com FAQ What are the profiles ofTrainers? Ourtrainerswho have8+ yearsofhandonexperienceinthesamedomain. How many studies material and Projects are covered in thecourse? Course has case studies and mini Project. Our course is designed by industryexperts. Coursefeaturesmanyrealtimeproblems.Pleaserefer course contentformoredetails. Do i need to carry my own laptop? What the software’srequired? Yes, you need to carry your own laptop. To start with, you need to install software in your system. Both of these are open source and in first class, trainer will helpyoutosetuptheenvironmentinyoursystem. Can i attend a Demo Session before enrollingfor thecourse? Yes, you can attend a free live Demo Session before enrolling for theCourse.
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