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Data Science And Analytics Course

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      Data Science And Analytics Course in Icon IT Inc

      The Data Science training course covers key topics such as data analysis, machine learning, and statistical modeling using industry-standard tools and technologies. We provide course materials, placement and visa assistance, accommodations

      Collaborative Learning and Career Building

      At the end of most Data Science And Analytics Course lessons, you'll have access to an online discussion. Engaging actively and constructively in these discussions can significantly boost your Data Science career development. By offering help or seeking assistance from the trainers, you’ll build meaningful relationships and create valuable professional connections.

      These discussions are more than just a conversation to share ideas—they're designed to accelerate your Data Science learning journey. That's why it is made an essential part of our courses: to support your growth and help you enhance your Data Science skills through collaboration and shared insights.

      Details to know about Data Science And Analytics Course

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      Who Can Take This Course

      Our data science course in the USA is designed to be flexible for all skill levels—beginners, intermediates, and advanced learners.

      This course suits beginners and experts, covers essential data analysis skills, and offers advanced topics for experienced learners across a wide range of data science topics, from statistics to machine learning.

      • Beginners: Those with no prior experience can start with foundational concepts and gradually build their skills.
      • Intermediate Learners: Individuals with some knowledge can deepen their understanding and tackle more complex topics.
      • Advanced Students: Experienced data scientists can refine their skills and explore specialized areas, ensuring continuous career growth.

      Trainers

      Data Science And Analytics Course's mentors are seasoned professionals from leading tech companies who bring real-world insights and practical experience to the table. They are Industry-leading experts who provide Career-focused learning and dedicated One-on-One Support.

      Our Renowned faculty from top universities hasdecades of experience in applied AI, machine learning, and deep learning applications. They provide deep theoretical knowledge, research-driven education, and insights into cutting-edge research.

      Our individualized career coaching and personalized learning paths ensure students receive tailored advice and support.

      Institution:

      Data Science And Analytics Course offers the best personalized learning experience with top industry-leading mentors and a cutting-edge curriculum designed to transform careers in data science.

      Duration:

      • Duration:6-9 months (Part-time and full time)
      • Format:Online and in-person Training
      • Our data science course includes 100 hours of comprehensive training

      Flexible Payment Options for Our Data Science Course

      Tuition Fees: $800 - $1500

      Payment plan:

      • Full Payment
      • Partial Payment
      • Installment Plan

      Location--- Biz Technix Inc.,6136 Frisco Square Blvd.,Suite #400 Frisco, Texas 75034

      Career opportunities:

      Here are the career opportunities after completing our data science course in the USA:

      • Data Scientist
      • Data Analyst
      • Data Engineer
      • Data Architect
      • Machine Learning Engineer
      • Business Intelligence Analyst

      Online and Offline

      Online Training

      • Interactor-led Online Training
      • Hands-on projects and exercises
      • Discussion forums and Q&A sessions
      • Access to a vast library of resources and materials

      Offline Training

      • Immersive workshops and seminars
      • Collaborative group projects and case studies
      • One-on-one mentorship and guidance from industry experts
      • Networking opportunities with fellow data science enthusiasts

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      Data Science And Analytics Course

      Introduction

      Our Data Science And Analytics Course is meticulously designed to provide comprehensive knowledge and hands-on experience in the fields of data science and analytics. This course is perfect for anyone looking to build a strong foundation in these rapidly growing fields and advance their career.

      Course curriculum:

      Our Data Science And Analytics Course curriculum is structured into comprehensive modules that cover a wide range of topics:

      Module 1: Foundations of Data Science

      Description:Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. In this first module we will introduce to the field of Data Science and how it relates to other fields of data like Artificial Intelligence, Machine Learning and Deep Learning.

      • Introduction to Data Science

      • High level view of Data Science, Artificial Intelligence & Machine Learning

      • Subtle differences between Data Science, Machine Learning & Artificial Intelligence

      • Approaches to Machine Learning

      • Terms & Terminologies of Data Science

      • Understanding an end to end Data Science Pipeline, Implementation cycle

      Module 2: Math for Data Science, Machine Learning and Artificial Intelligence Description: Mathematicsis veryimportantin the field ofdata scienceas concepts within

      mathematicsaid in identifying patterns and assist in creating algorithms. The understanding of various notions of Statistics and Probability Theory are key for the implementation of such algorithms indata science.

      • Linear Algebra

      • Matrices, Matrix Operations

      • Eigen Values, Eigen Vectors

      • Scalar, Vector and Tensors

      • Prior and Posterior Probability

      • Conditional Probability

      • Calculus

      • Differentiation, Gradient and Cost Functions

      • Graph Theory

      Module 3: Statistics for Data Science

      Description:This module focuses on understanding statistical concepts required for Data Science, Machine Learning and Deep Learning. In this module, you will be introduced to the estimation of various statistical measures of a data set, simulating random distributions, performing hypothesis testing, and building statistical models.

      Descriptive Statistics

      • Types of Data (Discrete vs Continuous)

      • Types of Data (Nominal, Ordinal)

      • Measures of Central Tendency (Mean, Median, Mode)

      • Measures of Dispersion (Variance, Standard Deviation)

      • Range, Quartiles, Inter Quartile Ranges

      • Measures of Shape (Skewness and Kurtosis)

      • Tests for Association (Correlation and Regression)

      • Random Variables

      • Probability Distributions

      • Standard Normal Distribution

      • Probability Distribution Function

      • Probability Mass Function

      • Cumulative Distribution Function

      Inferential Statistics

      • Statistical sampling & Inference

      • Hypothesis Testing

      • Null and Alternate Hypothesis

      • Margin of Error

      • Type I and Type II errors

      • One Sided Hypothesis Test, Two-Sided Hypothesis Test

      • Tests of Inference: Chi-Square, T-test, Analysis of Variance

      • t-value and p-value

      • Confidence Intervals

      Module 4: Python for Data Science Python for Data Science

      • Numpy

      • Pandas

      • Matplotlib & Seaborn

      • Jupyter Notebook

      Numpy

      NumPy is a Python library that works with arrays when performing scientific computing with Python. Explore how to initialize and load data into arrays and learn about basic array manipulation operations using NumPy.

      • Loading data with Numpy

      • Comparing Numpy with Traditional Lists

      • Numpy Data Types

      • Indexing and Slicing

      • Copies and Views

      • Numerical Operations with Numpy

      • Matrix Operations on Numpy Arrays

      • Aggregations functions

      • Shape Manipulations

      • Broadcasting

      • Statistical operations using Numpy

      • Resize, Reshape, Ravel

      • Image Processing with Numpy

      Pandas

      Pandas is a Python library that provides utilities to deal with structured data stored in the form of rows and columns. Discover how to work with series and tabular data, including initialization, population, and manipulation of Pandas Series and DataFrames.

      • Basics of Pandas

      • Loading data with Pandas

      • Series

      • Operations on Series

      • DataFrames and Operations of DataFrames

      • Selection and Slicing of DataFrames

      • Descriptive statistics with Pandas

      • Map, Apply, Iterations on Pandas DataFrame

      • Working with text data

      • Multi Index in Pandas

      • GroupBy Functions

      • Merging, Joining and Concatenating DataFrames

      • Visualization using Pandas

      Data Visualization using Matplotlib

      Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK+

      • Anatomy of Matplotlib figure

      • Plotting Line plots with labels and colors

      • Adding markers to line plots

      • Histogram plots

      • Scatter plots

      • Size, Color and Shape selection in Scatter

      • Applying Legend to Scatter plots

      • Displaying multiple plots using subplots

      • Boxplots, scatter_matrix and Pair plots

      Data Visualization using Seaborn

      Seaborn is a data visualization library that provides a high-level interface for drawing graphs. These graphs are able to convey a lot of information, while also being visually appealing.

      • Basic Plotting using Seaborn

      • Violin Plots

      • Box Plots

      • Cat Plots

      • Facet Grid

      • Swarm Plot

      • Pair Plot

      • Bar Plot

      • LM Plot

      • Variations in LM plot using hue, markers, row and column

      Module 5: Exploratory Data Analysis

      Exploratory Data Analysis helps in identifying the patterns in the data by using basic statistical methods as well as using visualization tools to displays graphs and charts. With EDA we can assess the distribution of the data and conclude various models to be used.

      Pipeline ideas

      • Exploratory Data Analysis

      • Feature Creation

      • Evaluation Measures

      Data Analytics Cycle ideas

      • Data Acquisition

      • Data Preparation

      • Data cleaning

      • Data Visualization

      • Plotting

        • Model Planning & Model Building

      Data Inputting

      • Reading and writing data to text files

      • Reading data from a csv

      • Reading data from JSON

      Data preparation

      • Selection and Removal of Columns

      • Transform

      • Rescale

      • Standardize

      • Normalize

      • Binarize

      • One hot Encoding

      • Imputing

      • Train, Test Splitting

      Module 6: Machine Learning

      In machine learning, computers apply statistical learning techniques to automatically identify patterns in data. This module on Machine Learning is a deep dive to Supervised, Unsupervised learning and Gaussian / Naive-Bayes methods. Also you will be exposed to different classification, clustering and regression methods.

      • Introduction to Machine Learning

      • Applications of Machine Learning

      • Supervised Machine Learning o Classification

      o Regression

      • Unsupervised Machine Learning

      • Reinforcement Learning

      • Latest advances in Machine Learning

      • Model Representation

      • Model Evaluation

      • Hyper Parameter tuning of Machine Learning

      • Evaluation of ML

      • Estimating and Prediction of Machine Learning Models

      • Deployment strategy of ML

      Module 7: Supervised Machine Learning – Classification

      Supervised learning is one of the most popular techniques in machine learning. In this module, you will learn about more complicated supervised learning models and how to use them to solve problems.

      Classification methods & respective evaluation

      • K Nearest Neighbors

      • Decision Trees

      • Naive Bayes

      • Stochastic Gradient Descent

      • SVM –

      • Linear

      • Non linear

      • Radial Basis Function

        • Random Forest

        • Gradient Boosting Machines

        • XGboost

        • Logistic regression

      Ensemble methods

      • Combining models

      • Bagging

      • Boosting

      • Voting

      • Choosing best classification method

      Model Tuning

      • Train Test Splitting

      • K-fold cross validation

      • Variance bias tradeoff

      • L1 and L2 norm

      • Overfit, underfit along with learning curves variance bias sensibility using graphs

      • Hyper Parameter Tuning using Grid Search CV

      Respective Performance measures

      • Different Errors (MAE, MSE, RMSE)

      • Accuracy, Confusion Matrix, Precision, Recall

      Module 8: Supervised Machine Learning - Regression

      Regression is a type of predictive modelling technique which is heavily used to derive the relationship between variables (the dependent and independent variables). This technique finds its usage mostly in forecasting, time series modelling and finding the causal effect relationship between the variables. The module discusses in detail about regression and types of regression and its usage & applicability

      Regression

      • Linear Regression

      • Variants of Regression o Lasso

      o Ridge

      • Multi Linear Regression

      • Logistic Regression (effectively, classification only)

      • Regression Model Improvement

      • Polynomial Regression

      • Random Forest Regression

      • Support Vector Regression

      Respective Performance measures

      • Different Errors (MAE, MSE, RMSE)

      • Mean Absolute Error

      • Mean Square Error

      • Root Mean Square Error

      Module 9: Unsupervised Machine Learning

      Unsupervised learning can provide powerful insights on data without the need to annotate examples. In this module, you will learn several different techniques in unsupervised machine learning.

      Clustering

      • K means

      • Hierarchical Clustering

      • DBSCAN

      Association Rule Mining

      • Association Rule

      • Market Basket Analysis using Apriori Algorithm

      • Dimensionality reduction using Principal Component analysis (PCA)

      Module 10: Natural Language Processing

      Natural language is essential to human communication, which makes the ability to process it an important one for computers. In this module, you will be introduced to natural language processing and some of the basic tasks.

      • Text Analytics

      • Stemming, Lemmatization and Stop word

      • POS tagging and Named Entity Recognition

      • Bigrams, Ngrams and colocations

      • Term Document Matrix

      • Count Vectorizer

      • Term Frequency and TF-IDF

      Module 11: Advanced Analytics

      Advanced Analytics covers various areas like Time series Analysis, ARIMA models, Recommender systems etc.

      Time series

      • Time series

      • ARIMA example

      Recommender Systems

      • Content Based Recommendation

      • Collaborative Filtering

      Module 12: Reinforcement Learning

      Reinforcement learningis an area ofMachine Learningwhich takes suitable action to maximize reward in a particular situation. It is employed by various software and machines to find the best possible behavior or path it should take in a specific situation.

      • Basic concepts of Reinforcement Learning

      • Action

      • Reward

      • Penalty Mechanism

      • Feedback loop

      • Deep Q Learning

      Module 13: Artificial Intelligence

      Artificial intelligence(AI) is the ability of a computer program or a machine to think and learn. It is also a field of study which tries to make computers "smart"

      Artificial Neural Networks

      • Neural Networks & terminologies

      • Non linearity problem, illustration

      • Perceptron learning

      • Feed Forward Network and Back propagation

      • Gradient Descent

      Mathematics of Artificial Neural Networks

      • Gradients

      • Partial derivatives

      • Linear algebra o Li

      • LD

      • Eigen vectors o Projections

        • Vector quantization

      Overview of tools used in Neural Networks

      • Tensor Flow

      • Keras

      Module 14: Deep Learning

      Deep learning is part of a broader family of machine learning methods based on the layers used in artificial neural networks. In this module, you’ll deep dive in the concepts of Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Auto Encoders and many more.

      Deep Learning

      • Tensorflow & keras installation

      • More elaborate discussion on cost function

      • Measuring accuracy of hypothesis function

      • Role of gradient function in minimizing cost function

      • Explicit discussion of Bayes models

      • Hidden Markov Models (HMM)

      • Optimization basics

      • Sales Prediction of a Gaming company using Neural Networks

      • Build an Image similarity

      Deep Learning with Convolutional Neural Nets

      • Architecture of CNN

      • Types of layers in CNN

      • Different Filters and Kernals

      • Building an Image classifier with and without CNN

      Recurrent neural nets

      • Fundamental notions & ideas o Recurrent neurons

      Handling variable length sequences

      • Training a sequence classifier

      • Training to predict Time series

      Module 15: Cloud Computing for Data Science

      Cloud computing is massively growing in importance in the IT sector as more and more companies are eschewing traditional IT and moving applications and business processes to the cloud. This section covers detailed information about how to deploy Data Science models on Cloud environments.

      Topics

      • Introduction to Cloud Computing

      • Amazon Web Services Preliminaries - S3, EC2, RDS

      • Big data processing on AWS using Elastic Map Reduce (EMR)

      • Machine Learning using Amazon Sage Maker

      • Deep Learning on AWS Cloud

      • Natural Language processing using AWS Lex

      • Analytics services on AWS Cloud

      • Data Warehousing on AWS Cloud

      • Creating Data Pipelines on AWS Cloud

      Module 16: DevOps for Data Science

      DevOps play a pivotal role in bridging the gap between Development and Operational teams. This section covers key DevOps tools which a Data Scientist need to be aware of for doing their day to day data science work.

      Demand of Data science job in USA

      According to the U.S. Bureau of Labor Statistics, data scientist positions will continue to be among the fastest-growing jobs in 2024. The projected increase in job openings from 2022 to 2032 is 35%.

      In addition, the World Economic Forum’s Future of Jobs 2023 report estimates that by 2027, the demand for AI and machine learning specialists will increase by 40%, and for data analysts, scientists, engineers, BI analysts, and other big data and database professionals will grow by 30%–35%.

      Data science tools you will learn

      Here are 7 crucial data science tools that covered in our comprehensive data science training program:

      Python

      R

      SQL

      TensorFlow

      Power BI

      Tableau

      Jupyter Notebook

      Microsoft Excel

      MATLAB

      Who can take up this "Data Science And Analytics Course" certification course

      Beginners

      Individuals with little to no prior knowledge of data science, programming, or statistics, can join this "Data Science And Analytics Course" certification course

      Intermediate

      Those who have some experience with data analysis or programming but want to deepen their understanding of data science concepts and tools. This level will also enhance their existing skills and introduce new tools and methodologies.

      Professionals

      Individuals already working in related fields (like data analysis, software development, or business intelligence) looking to transition into data science or enhance their current skill set or those looking for promotion.

      Which city is best for data science jobs in USA?

      Some of the demanding Cities for Data Scientist in USA.

      San Francisco,

      CA San Jose, CA

      Oakland, CA

      Fremont, CA

      Chandler, AZ

      Pittsburgh, PA

      Los Angeles, CA

      Among these, Silicon Valley, CA area is home to some of the most famous technology companies in the world, including Meta (home of Facebook and Instagram), Apple, and Google. There are also research-focused data science jobs in the area through Stanford University and NASA's Ames Research Center.

      Which state has best opportunities for Data Scientist in USA?

      There are various states in USA offers wider data science career opportunities, one among them is California.

      The state boasts an abundance of tech giants, start-ups, and innovative companies, offering a plethora of opportunities for those skilled in data science. With a high demand for talent and competitive salaries, California remains a top choice for data scientists looking to advance their careers.

      Target Audience

      The Data Science And Analytics Course is ideal for:

      • Aspiring data scientists and analysts
      • Professionals looking to switch careers to data science and analytics
      • Students and graduates seeking to enhance their skills
      • IT professionals aiming to expand their knowledge in data analytics

      Course Objectives

      By enrolling in Data Science And Analytics Course, you will:

      • Gain a deep understanding of data science and analytics principles.
      • Master essential tools and technologies used in the industry.
      • Develop the ability to analyze and interpret complex data.
      • Learn to create predictive models and perform data-driven decision-making.
      • Be prepared for industry-recognized certifications in data science and analytics.

      Learning Methodology

      Our Data Science And Analytics Course employs a blend of instructional methods to ensure a thorough understanding of concepts:

      • Live Online Classes: Interactive sessions with expert instructors.
      • Self-Paced Learning: Access to recorded lectures and materials.
      • Hands-On Projects: Practical exercises and real-world projects.
      • Collaborative Learning: Group discussions and peer feedback.

      Instructors

      Learn from industry-leading experts with years of experience in data science and analytics:

      Hands-On Projects

      Gain practical experience by working on projects such as:

      • Predictive Modeling: Develop models to predict outcomes based on data.
      • Data Visualization: Create visualizations to communicate data insights.
      • Big Data Processing: Handle and analyze large datasets using Hadoop and Spark.

      Certification

      Upon completing the Data Science And Analytics Course, you will receive a certification that demonstrates your proficiency in data science and analytics. This certificate can enhance your resume and LinkedIn profile, showcasing your skills to potential employers.

      Prerequisites

      No prior experience is required to start this course. Basic knowledge of mathematics and statistics is helpful but not mandatory.

      Enrollment Process

      Enrolling our Data Science And Analytics Course is easy:

      1. Visit Our Website:https://techjobs.sulekha.com/data-science-training
      2. Complete the Application: Fill out the online registration form.

      Career Opportunities

      Completing this Data Science And Analytics Course opens doors to various career paths:

      • Data Scientist
      • Data Analyst
      • Machine Learning Engineer
      • Business Intelligence Analyst

      Support Services

      Data Science And Analytics Course offer additional support to help you succeed:

      • Mentorship: Guidance from experienced data scientists.
      • Career Services: Job placement support, resume writing assistance, and interview preparation.
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      Data Science And Analytics Course Provided By Icon IT Inc | Data Science institutes
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      Data Science And Analytics Course Provided By Icon IT Inc | Data Science institutes
      Online
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      Data Science And Analytics Course Provided By Icon IT Inc | Data Science institutes
      Online
      500

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      Data Science FAQs

      What skills or experience should I have before starting to learn data science?

      Anyone can start learning data science without prior experience. Basic computer skills and an interest in data are helpful but not required. Those with some knowledge of statistics and coding may skip introductory courses.

      How can I determine if pursuing a career in data science is the right choice for me?

      Pursuing a career in data science is rewarding for analytical thinkers who enjoy coding and working with data. Data scientists need to learn various programming languages, use data systems and tools for analysis, and solve problems with data. Strong communication skills are crucial for collaborating with teams and sharing insights.

      Should I pursue a Data Science course, certification, or degree? How do I determine which option is best for me?

      Beginners should consider courses and Professional Certificates, which require less time and money. Start with "What is Data Science?" (part of Data Science Professional Certificate) or a course like "Introduction to Statistics." Both are great for career switchers to acquire the necessary skills for an entry-level job.

      What tasks and roles does a data scientist typically handle?

      A data scientist performs research to discover patterns in data, manages and examines both structured and unstructured information on a large scale, utilizes technology to forecast intricate data sets, and develops algorithms to gather and analyze data.

      What are the essential skills needed to become a data scientist?

      Data scientists possess extensive technical expertise in areas such as computer programming, data mining, artificial intelligence, and predictive analytics, enabling them to efficiently organize and analyze data. While technical proficiency is crucial in this field, it is equally important for data scientists to develop strong soft skills, particularly in effective communication.

      Is it challenging to learn data science?

      With Icon IT Inc's resources, courses, and expert instructors, mastering data science becomes straightforward and self-paced. While a background in coding and mathematics can be beneficial, beginners with no prior experience can also quickly grasp the fundamentals through Icon IT Inc's introductory courses or boot camps.

      What is the typical duration to finish a data science course?

      In an introductory course or boot camp, students can grasp the fundamentals of data science within a few weeks. Conversely, comprehensive programs such as undergraduate or master's degrees can span 2-3 years, offering a thorough exploration of core concepts, programming languages, and machine learning.

      Is data analytics the same as data science?

      Data Analytics and Data Science not exactly same. Data analytics focuses on analyzing datasets to find patterns and insights. Data science encompasses this and further uses programming, math, and statistics to identify trends, solve problems, and create solutions. Many data scientists start as data analysts.

      What topics are generally included in data science courses?

      Data science courses cover essential topics such as statistics, probability, data visualization, machine learning, data mining, and data wrangling. Advanced courses may include big data technologies, deep learning, and tools like Python, R, and SQL. Practical projects and case studies provide hands-on experience in applying these concepts to real-world problems.

      What career paths are available with a data science certificate?

      Earning a data science certificate can open numerous career opportunities in various industries, such as technology, finance, healthcare, and marketing. Common roles include data scientist, data analyst, machine learning engineer, and business intelligence analyst. These jobs involve analyzing large datasets, building predictive models, and supporting data-driven decision-making, significantly enhancing your career prospects and advancement potential.

      What coding languages are most commonly used in data science?

      Python is the most popular programming language for data science, known for its versatility and beginner-friendly nature. R is also popular, especially for statistical analysis. Proficiency in SQL is crucial, as it's used for managing data in relational databases. While Python, R, and SQL are the main languages in data science, languages like Java, C++, JavaScript, and Scala are also used. Familiarity with any programming language can help you quickly learn new ones.