Data Science Foundation

Master the art of data manipulation, exploration, and presentation by gaining proficiency in Python and SQL, allowing you to clean, analyze, and visualize datasets with precision and effectiveness. Elevate your skills to navigate the intricacies of data science, unlocking a world of insights and strategic decision-making.

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  7. (535 ratings)
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  • English
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Data Science Foundation

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    • 1000+ learner
    • 1000+ learner
    • 1000+ learner
    • 1000+ learner
    {{ $page->student_count }}+ Learners
Features
  • {{$page->content_hours}}+ Hours on-demand Video
  • Certificate of completion
  • Projects Included
  • Taught by Industry Pros
Enroll Now

What you'll learn

Data Science stands out as a highly sought-after skill in today's job landscape, with companies consistently recruiting individuals with expertise in this field. This Skill Path is designed to provide a solid foundation in data cleaning, analysis, and visualization. You'll master essential tools like Python, pandas, and SQL – widely recognized in the industry. As you progress, hands-on projects will be integral to your learning, offering practical experience and a platform to demonstrate your analytical proficiency in real-world scenarios.

Data Science Foundation

Best-in-class content by leading faculty and industry leaders with over a decade of working experience in the form of cases, videos and assignments, projects, & live sessions.

best data science courses Noida

Prerequisites

  • 1. No programming experience required.
  • 2. Interested in using data to make better business decisions.

Course Details

Engage in data analysis and statistical interpretation using Python, allowing for a comprehensive understanding of datasets. Proficiently read and write databases using SQL to manipulate and manage data effectively. Generate meaningful data visualiza tions to communicate insights clearly and facilitate informed decision-making. Cultivate a strong Data Science portfolio that highlights your expertise and demonstrates the practical application of your skills in real-world scenarios.

Certificate

Get industry Recognize certificate of completion in Business Analytics with Tableau course form DataTrained education which can we varified online through out the world, shared in certification section on your linked profile, downloaded, priented, shared and mentined on your resume.

Reviews

Riley Bennett
  1. 4.5

The course provides an excellent overview. It offers a comprehensive and easily understandable description of each concept. The content is presented in a way that simplifies complex ideas, making it accessible and straightforward for learners.

Ethan Marshall
  1. 4.5

The instructor possesses a strong grasp of the subject and articulates concepts with great clarity. The course features excellent content, making it highly recommended to everyone.

Isla Matthews
  1. 4.5

I came across a Facebook ad from DataTrained and immediately reached out to them to inquire about their online course. Their counselor guided me through the entire program, providing insights into what Data Science entails. After several weeks of ongoing discussions, I gained a clear understanding of the course and confidently identified it as a worthwhile investment for my money and effort.

Joseph Graham
  1. 4.5

Since joining DataTrained, my learning curve has steepened considerably. Under the guidance of mentors, I embraced a new approach to secure a job in Data Science. If I am fortunate enough to land a position with one of the leading data science firms, I owe all credit to DataTrained and their skilled faculty.

Instructors

Join DataTrained certified curriculum and learn every skill from the industry’s best thought leaders.

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Jay Rajasekharan - Data Scientist, IBM

Jay Rajasekharan

Data Scientist, IBM

Currently, he is driving several productivity programs - using data analytics to drive insights from business operations and implementing optimizations such as streamlining workflows, improving service levels, and ultimately reducing cost.

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Dr. Deepika Sharma - Training Head, DataTrained

Dr. Deepika Sharma

Training Head, DataTrained

Dr. Deepika Sharma has been associated with academics /corporate education for more than 14 years. She has a deep passion in the field of Artificial Intelligence, Data Science, and Machine Learning.

Shankargouda Tegginmani - Data Scientist, Accenture

Shankargouda Tegginmani

Data Scientist, Accenture

Shankar is a data Scientist with 14 Years of Experience. His current employment is with Accenture and has experience in telecom, healthcare, finance and banking products.

Curriculum

  • 100+ Hours of Content - data science program institute
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    Hours of Content

  • 100+ Live Sessions - data science online training
  • 100+

    Live Sessions

  • 100+ Live Sessions - data science online training
  • 15

    Tools and Software

Comprehensive Curriculum

The curriculum has been designed by faculty from IITs, and Expert Industry Professionals.

100+ Hours of Content - data science program institute
{{$page->content_hours}}+

Hours of Content

100+ Live Sessions - data science online training
100+

Live Sessions

15 Tools and Software - best tools for data science
15

Tools and Software

Module 1 {{--

What is data?

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Data refers to raw facts, figures, or information that is typically in a form that can be processed or analyzed.Data is collected and used for various purposes, including analysis, decision-making, and gaining insights. In the context of computing and technology, data is often organized and structured to be stored and processed efficiently.

There are two main types of data:

Structured Data: Data that is organized in a predefined manner, usually in rows and columns, making it easy to search, query, and analyze. Examples include databases and spreadsheets.

Unstructured Data: Data that lacks a predefined data model or structure. It includes text, images, videos, and other forms of information that are not organized in a specific way. Analyzing unstructured data may require advanced techniques such as natural language processing.

In the field of data science and analytics, the process of extracting useful information and knowledge from data is known as data analysis. The importance of data has grown significantly in various domains, as organizations use it to make informed decisions, identify trends, and gain a deeper understanding of various phenomena.

Module 2 {{--

What is data?

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SQL is a programming language designed for managing and manipulating relational databases. It is a standard language used for interacting with databases, allowing users to perform various operations such as querying data, updating records, inserting new data, and deleting information.

Module 3 {{--

What is data?

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Embark on a transformative exploration into the realm of data science by honing your skills in Python. Kickstart your journey with the art of data wrangling, mastering the adept cleaning of datasets. Dive into the captivating world of data visualization, acquiring the essential skills to manipulate and present data with precision. Lay a robust foundation for your expertise in the ever-evolving field of data science.

Module 4 {{--

What is data?

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Pandas is a powerful and open-source Python library designed for efficient data manipulation and analysis. It introduces two fundamental data structures, the DataFrame and Series, which serve as versatile tools for handling and processing data. A DataFrame represents a structured, two-dimensional table with rows and columns, akin to a spreadsheet, while a Series is a one-dimensional labeled array capable of storing diverse data types.

Widely adopted in data science, machine learning, and various analytical domains, Pandas excels in tasks such as data cleaning, reshaping, merging, slicing, indexing, and statistical analysis. Its seamless integration with other Python libraries, such as NumPy, Matplotlib, and scikit-learn, enhances its flexibility and utility, making Pandas an indispensable tool for data-centric operations in Python.

Module 5 {{--

What is data?

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The foundation of Data Science is deeply rooted in mathematics, encompassing crucial areas like statistics, linear algebra, and calculus. These mathematical principles serve as the cornerstone for crafting algorithms, constructing models, and performing analyses, enabling the extraction of meaningful insights from intricate datasets.

Within the realm of Data Science, key mathematical concepts include:

    1. Probability: Fundamental for addressing uncertainty and randomness within data.

    2. Bayes Theorem: A potent tool for updating probabilities based on new information or evidence.

    3. Distribution: Understanding the dispersion and patterns within a dataset's values.

    4. Descriptive Statistics & Outliers Treatment: Summarizing and interpreting data, while addressing anomalies that could impact analysis.

    5. Hypothesis Testing & AB Testing: Evaluating assumptions about data and conducting experiments for insightful conclusions.

    6. ANOVA (Analysis of Variance): Assessing variations between groups in a dataset for informed comparisons.

    7. Correlation: Examining relationships and dependencies among different variables in the data.

By mastering these mathematical concepts, Data Scientists enhance their ability to create robust models, conduct sophisticated analyses, and ultimately draw valuable insights from diverse and complex datasets.

Module 6 {{--

What is data?

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Data wrangling, also known as data munging, is the process of cleaning, structuring, and organizing raw data into a format suitable for analysis. This phase is crucial in the overall data preparation workflow, as it helps ensure that the data is in a usable and meaningful state for further exploration and analysis.

Industry Projects

Learning through real-life industry projects sponsored by top companies is a practical and effective way to gain hands-on experience and develop practical skills. Project-based learning bridge the gap between theory and application.

  • Data science program Engage in collaborative projects and learn from peers
  • Data science programMentoring by industry experts to learn and apply better
  • Data science programPersonalized subjective feedback on your submissions to facilitate improvement
Smartphone and Smartwatch Activity - data science training in india

Smartphone and Smartwatch Activity

The crude accelerometer and whirligig sensor information is gathered from the cell phone and smartwatch at a pace of 20Hz.

Recommendation System - best online data science programs

Recommendation System

In the connected world, it is imperative that the organizations are using to Recommend their Products & Services to the People.

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Air Quality Study

Based on The Data Collected from the Meteorological Department, Predicting The Air Quality Of Different Parts of The country

Admission Process

There are 3 simple steps in the Admission Process which is detailed below

Step 1: Fill in a Query Form

Fill up the Query Form and one of our counselors will call you & understand your eligibility.

Step 2: Get Shortlisted & Receive a Call

Our Admissions Committee will review your profile. Upon qualifying, an Email will be sent to you confirming your admission to the Full Stack Development Engineering.

Step 3: Block your Seat & Begin the Prep Course

Block your seat with payment to enroll in the program. Begin with your Prep course and start your Full Stack Development journey!

Full Stack Development Engineering Course Fee

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No Cost EMI options are also available. *

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What's Included in the Price

Industry recognized certificate from GoDaddy and DataTrained

Access to real-life industry-grade projects

6 Months live Internship as part of the core curriculum

For Queries and Suggestions

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Frequently Ask Questions

The Python Data science course offered by DataTrained teaches you the necessary skills to master the concept of Python programming for data science. Through our course you would learn Data Analysis, Machine Learning algorithms, Data Visualization , Natural Language Processing & Web Scraping.

Yes, while data science does demand fundamental coding skills, one doesn't necessarily need to reach an advanced coding level. In certain scenarios, coding may not be essential, thanks to user-friendly interfaces like Google AutoML. Python, being the most widely used programming language in the realm of data science, is comparable to learning the English language for those entering this field.

Another benefit of python is that there are already libraries available which mean you don’t need to write codes. Therefore, during data science training, the focus will be on learning data science concepts, applications and projects. You don’t have to worry about coding. Our program does provide all the necessary skills required for data science like the basics of python.

Yes, Data science is a good career. Data science is a buzzword now. Harvard Business declared data scientist as the sexiest job of the 21st century The Data science sector grew 650% since 2012 and the future is even better since data has surpassed oil in value. The US Bureau of Labor Statistics forecasted that demand for data science skills will increase by 27.9% by 2026. The benefits of a career in data science are -"

  • High Demand for Data Scientists
  • Less Competition
  • High Pay
  • Opportunities for Growth
  • Variety of job roles

The future is bright and promising in the data science career. Get this opportunity and be future-ready with our data science online course.

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Yes, Coding is required in business. Although you don’t have to be an advanced coder to be a business analyst. Now there are tools that help you build models exactly as same as you could do through coding. you don’t even write a single code since there are so many interfaces like google AutoML. Python, which is the most popular programming in the world of data science. Learning the Python language is like learning the English language.

Our program on Business Analytics with Tableau prerequisites are:

  • No programming experience is required.
  • Interested in using data to make better business decisions.
  • You should take this course if want to learn Tableau completely from scratch
  • You should take this course if you are good with Tableau and want to take your skills to the next level and truly leverage the full potential of Tableau

There are 5 steps for business analytics:-

  • Discover business need
  • Collect data
  • Analysis of data
  • Prediction
  • Making a decision

There are 4 types of business analytics:-

  • Descriptive Analytics
  • Diagnostic Analytics
  • Predictive Analytics
  • Prescriptive Analytics

Business analytics tools are applications that extract data from one or more business systems and consolidate it within a repository, like a data warehouse, for review and analysis. Most businesses utilize many analytics tools, such as spreadsheets with statistical capabilities, statistical software packages, complex data mining tools, and predictive modeling software.

These business analytics tools, when used together, provide a comprehensive perspective of the firm, allowing for vital insights and understanding of the organization to be gained, allowing for better decisions to be made about business operations, customer conversions, and more.

Business analytics tools are:

For Data mining
  • R environment
  • Scikit- Learn (Python)
  • Rapid Miner
For Quantitative Analytics
  • Numpy (Python)
  • DataWrangler
  • Tableau Public
For Qualitative Analytics
  • Nvivo
  • Quirkos
  • Datagrav
For Business Intelligence
  • Tableau
  • SAS
  • Oracle
For Predictive Analytics
  • Microsoft R
  • R environment
  • Anaconda
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