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Data Science Course

Data Science is the study of data that use different scientific methods, records, processes, and analysis to extract any type of data for making useful information. Learn Data Science in real-time with ITGuru experts practically with live training.

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Course Overview

The Data Science Online Training at IT Guru will provide you the best knowledge on Data Science basics, data analysis, basic statistics, etc with live experts. Learning Data Science Online Course makes you a master in this subject that includes statistical methods, data analysis, ML algorithms, & models, applications, languages & tools, etc. Our best Data Science Course Online module will provide you a way to become certified in Data Science. So, join hands with ITGuru for accepting new challenges and make the best solutions through the best Online Data Science Courses. The Data Science Course basics and other features will make you an expert in the data analysis, tools, Data Science framework, etc to deal with real-time tasks. IT Guru provides the Data Science Online Training Hyderabad, where you will come to know how Data Science is applicable in different fields/businesses. The Data Science Online Training India with IT Guru will help you to get your training easily with the updates and will make you certified and skillful in the Data Science platform.

Data Science Course Objectives

  • Who can take the Data Science online course?

    The Data Science course runs behind skills like Mathematics, Statistics, Algebra, Probabilities, etc. It is connected with different platforms like Data management, Databases, ML Algorithms, Stats, Software development, etc. Thus, this course is for Developers, Business Analysts, Analytics Managers, etc.

  • What are the prerequisites for Data Science Online Training?

    To learn Data Science one should have basic knowledge of Mathematics, Statistics, Aptitude, etc. Along with these having an idea of Algorithms, Coding, SQL, etc will help to understand the subject easily.

  • Why should you learn the best Data Science Course Online?

    The Data Science Course will help you to get practical knowledge of Data Science, data analysis, data types, etc with real-time examples to get expert knowledge.

  • What do you learn in the Data Science Online Training?

    The Data Science Online Training from IT Guru will give you real-time industry experience from Data Science experts on its usage, other technological aspects, etc.

  • What are the benefits of the best Online Data Science Courses?

    After getting certified in our Data Science Course, you can expect yourself to work in a better position within a company with a better payout.

Data Science Online Course Key Features

  • Lifetime Access

    You get lifetime access to the Learning Management System (LMS) where presentations, assignments, and installation guide on Data Science Certification.

  • Assignments

    Trainers will assign some assignments soon after the completion of each and every topic that makes you master in the Data Science Online Course and also helps you to clear Data Science Certification

  • Real-life Case Studies

    ITGuru trainers teach you each and every topic with real-world case studies that makes the learner understand in a better way

  • 24 x 7 Support

    We have 24x7 online support team to resolve all your queries

  • Certification

    IT Guru team has designed the Data Science Online Course in the way to clear Certification as per the latest syllabus to make your dream come true.

  • Job Assistance

    IT Guru supports learners in finding job opportunities with the newly acquired skill set. Online IT Guru has a varied bunch of Clientele around the globe, over 200+ companies in various countries like the USA and India. Soon after the completion of the course, the support team will pass your resumes to the companies and ensure that the learners will achieve 100% placements.

Data Science Online Course Syllabus

  • Module 1: Introduction to DataScience
    • What is Data Science?
    • Why Python for data science?
    • Relevance in industry and need of the hour
    • How leading companies are harnessing the power of Data Science with Python?
    • Different phases of a typical Analytics/Data Science projects and role of python
    • Anaconda vs. Python
  • Module 2: Python Essentials (Core)
    • Python Datatypes Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
    • Functions
    • Exceptions
    • Decarators
    • Classes and Inheritance
    • Multithreading
    • Python with Databases (PostgresSQL, MySQL)
  • Module 3: Accessing / Importing and Exporting Data using Python Modules
    • Importing Data from various sources (Csv, txt, excel, access etc)
    • Database Input (Connecting to database)
    • Viewing Data objects - subsetting, methods
    • Exporting Data to various formats
    • Important python modules: Pandas, beautifulsoup
  • Module 4: Data Analysis and Visualization using Python
    • Introduction exploratory data analysis
    • Descriptive statistics, Frequency Tables and summarization
    • Univariate Analysis (Distribution of data & Graphical Analysis)
    • Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
    • Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc)
    • Important Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, seaborn, Pandas and scipy.stats etc)
    • Libraries we focus under module 4
    • Numpy - Numerical library
    • a) ND array
    • b) Subset, slicing
    • c) Indexing
    • d) List vs ND array
    • e) Manipulating arrays
    • f) Mathematical operations and apply functions
    • g) Linear algebra operations
    • Scipy – Scientific Lirary
    • Pandas - Data Analysis library
    • a) Data loading
    • b) Series and Data frame
    • c) Selecting rows and columns
    • d) Position and label-based indexing
    • e) Slicing and dicing
    • f) Merging and concatenating
    • g) Grouping and summarizing
    • h) Data Processing, cleaning
    • i) Missing Values
    • j) Outliers
    • Matplotlib – Basic 2D Data Visualization library
    • a) Introduction to Matplotlib Basic plotting Figures and sub plotting
    • Box plot, Histograms, Scatter plots, image loading
    • b) Introduction to Seaborn
    • Histogram, rugged plot, hex plot and density plot
    • Joint plot, pair plot, count plot, Heat maps
    • c) Plotting categorical data and aggregation
    • Seaborn – Advanced Data Visualization library
    • Stat – Stastics library
  • Module 5: Statistics & MathMatics
    • Types of data
    • Levels of measurement 
    • Categorical variables. Visualization techniques for categorical variables 
    • Numerical variables. Using a frequency distribution table 
    • Histogram charts 
    • Cross tables and scatter plots 
    • Measures of central tendency
    • The main measures of central tendency: mean, median and mode 
    • Measuring skewness 
    • Measuring how data is spread out: calculating variance 
    • Standard deviation and coefficient of variation 
    • Calculating and understanding covariance 
    • The correlation coefficient 
    • Basic Statistics - Measures of Central Tendencies and Variance
    • Building blocks - Probability Distributions - Normal distribution - Central Limit Theorem
    • Inferential Statistics -Sampling - Concept of Hypothesis Testing
    • Statistical Methods - Z/t-tests (One sample, independent, paired), Anova, Correlation and Chi-square
    • Important modules for statistical methods: Numpy, Scipy, Pandas
  • Module 6: Machine Learning – Predictive Modelling – Basics
    • Introduction to Machine Learning & Predictive Modeling
    • Types of Business problems - Mapping of Techniques - Regression vs. classification vs. segmentation vs. Forecasting
    • Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
    • Different Phases of Predictive Modeling (Data Pre-processing, Sampling, Model Building, Validation)
    • Overfitting (Bias-Variance Trade off) & Performance Metrics
    • Feature engineering & dimension reduction
    • Concept of optimization & cost function
    • Concept of gradient descent algorithm
    • Concept of Cross validation(Bootstrapping, K-Fold validation etc)
    • Model performance metrics (R-square, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, confusion metrics )
  • Module 7: Machine Learning Algorithms & Applications – Implementation in Python
    • Linear & Logistic Regression
    • Segmentation - Cluster Analysis (K-Means)
    • Decision Trees (CART/CD 5.0)
    • Ensemble Learning (Random Forest, Bagging & boosting)
    • Artificial Neural Networks(ANN)
    • Support Vector Machines(SVM)
    • Other Techniques (KNN, Naïve Bayes, PCA)
    • Introduction to Text Mining using NLTK
    • Introduction to Time Series Forecasting (Decomposition & ARIMA)
    • Important python modules for Machine Learning (SciKit Learn, stats models, scipy, nltk etc)
    • Fine tuning the models using Hyper parameters, grid search, piping etc.
  • Machine Learning Case Studies
    • Market Basket Analysis
    • Dimensionality reduction on CTG
    • Email filtering – spam or not spamd
    • Product recommendations
    • Fraud detection
    • Breast cancer diagnostic detection
    • House price prediction analysis
    • Predicting wine quality
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Data Science Course Projects

Data Science Course Training Options

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    Self-Paced Learning

    16465 18500

    Get Free Trial
    This course includes
    • 60 hours high-quality video
    • 2 projects
    • 18 downloadable resource
    • Lifetime access and 24x7 support
    • Access on your computer or mobile
    • Get certificate on course completion
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    Live Online Training

    • High-quality content created by industry experts
    • Lifetime access to high-quality self-paced learning and live online class recordings
    • Flexible, affordable options
    • Get complete certification guidance
    • 24x7 assistance and support
    • Attend a Data Science Online Course free demo before signing up
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    Corporate Training

    • Self-Paced / Live Online training options
    • Flexible, affordable options
    • Learn as per full day schedule and/or flexible timings
    • Customise your own course content based on your project requirements
    • Get complete certification guidance
    • 24x7 assistance and support

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