Anil  Sakhiya

Anil Sakhiya

1618387620

Python for Data Science | Data Science Tutorial For Beginners in 2021

Great Learning brings you this live session on “Python for Data Science”. In this session, you will be learning about Python and its importance in the domain of Data Science. Python is one of the world’s most popular programming languages out there right now. It has a huge impact on the world through many domains and Data Science is one such domain where the power and capability of Python are showcased very well. It helps to solve complex problems in a simple manner by harbouring the advantages of a versatile programming language. Since Data Science is a vast domain and Python has numerous applications, as a learner, you should have a structured understanding of how you can learn Python in the best manner possible. The instructor also covers the important aspects of Python for Data Science, a demonstration with a real-life dataset and also shares advice on the factors you need to concentrate on when learning Python for Data Science.

#python #data-science

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Python for Data Science | Data Science Tutorial For Beginners in 2021
Ray  Patel

Ray Patel

1619518440

top 30 Python Tips and Tricks for Beginners

Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.

1) swap two numbers.

2) Reversing a string in Python.

3) Create a single string from all the elements in list.

4) Chaining Of Comparison Operators.

5) Print The File Path Of Imported Modules.

6) Return Multiple Values From Functions.

7) Find The Most Frequent Value In A List.

8) Check The Memory Usage Of An Object.

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Uriah  Dietrich

Uriah Dietrich

1618449987

How To Build A Data Science Career In 2021

For this week’s data science career interview, we got in touch with Dr Suman Sanyal, Associate Professor of Computer Science and Engineering at NIIT University. In this interview, Dr Sanyal shares his insights on how universities can contribute to this highly promising sector and what aspirants can do to build a successful data science career.

With industry-linkage, technology and research-driven seamless education, NIIT University has been recognised for addressing the growing demand for data science experts worldwide with its industry-ready courses. The university has recently introduced B.Tech in Data Science course, which aims to deploy data sets models to solve real-world problems. The programme provides industry-academic synergy for the students to establish careers in data science, artificial intelligence and machine learning.

“Students with skills that are aligned to new-age technology will be of huge value. The industry today wants young, ambitious students who have the know-how on how to get things done,” Sanyal said.

#careers # #data science aspirant #data science career #data science career intervie #data science education #data science education marke #data science jobs #niit university data science

akshay L

akshay L

1610872689

Data Science With Python Training | Python Data Science Course | Intellipaat

In this Data Science With Python Training video, you will learn everything about data science and python from basic to advance level. This python data science course video will help you learn various python concepts, AI, and lots of projects, hands-on demo, and lastly top trending data science and python interview questions. This is a must-watch video for everyone who wishes o learn data science and python to make a career in it.

#data science with python #python data science course #python data science #data science with python

Sival Alethea

Sival Alethea

1624305600

Learn Data Science Tutorial - Full Course for Beginners. DO NOT MISS!!!

Learn Data Science is this full tutorial course for absolute beginners. Data science is considered the “sexiest job of the 21st century.” You’ll learn the important elements of data science. You’ll be introduced to the principles, practices, and tools that make data science the powerful medium for critical insight in business and research. You’ll have a solid foundation for future learning and applications in your work. With data science, you can do what you want to do, and do it better. This course covers the foundations of data science, data sourcing, coding, mathematics, and statistics.
⭐️ Course Contents ⭐️
⌨️ Part 1: Data Science: An Introduction: Foundations of Data Science

  • Welcome (1.1)
  • Demand for Data Science (2.1)
  • The Data Science Venn Diagram (2.2)
  • The Data Science Pathway (2.3)
  • Roles in Data Science (2.4)
  • Teams in Data Science (2.5)
  • Big Data (3.1)
  • Coding (3.2)
  • Statistics (3.3)
  • Business Intelligence (3.4)
  • Do No Harm (4.1)
  • Methods Overview (5.1)
  • Sourcing Overview (5.2)
  • Coding Overview (5.3)
  • Math Overview (5.4)
  • Statistics Overview (5.5)
  • Machine Learning Overview (5.6)
  • Interpretability (6.1)
  • Actionable Insights (6.2)
  • Presentation Graphics (6.3)
  • Reproducible Research (6.4)
  • Next Steps (7.1)

⌨️ Part 2: Data Sourcing: Foundations of Data Science (1:39:46)

  • Welcome (1.1)
  • Metrics (2.1)
  • Accuracy (2.2)
  • Social Context of Measurement (2.3)
  • Existing Data (3.1)
  • APIs (3.2)
  • Scraping (3.3)
  • New Data (4.1)
  • Interviews (4.2)
  • Surveys (4.3)
  • Card Sorting (4.4)
  • Lab Experiments (4.5)
  • A/B Testing (4.6)
  • Next Steps (5.1)

⌨️ Part 3: Coding (2:32:42)

  • Welcome (1.1)
  • Spreadsheets (2.1)
  • Tableau Public (2.2)
  • SPSS (2.3)
  • JASP (2.4)
  • Other Software (2.5)
  • HTML (3.1)
  • XML (3.2)
  • JSON (3.3)
  • R (4.1)
  • Python (4.2)
  • SQL (4.3)
  • C, C++, & Java (4.4)
  • Bash (4.5)
  • Regex (5.1)
  • Next Steps (6.1)

⌨️ Part 4: Mathematics (4:01:09)

  • Welcome (1.1)
  • Elementary Algebra (2.1)
  • Linear Algebra (2.2)
  • Systems of Linear Equations (2.3)
  • Calculus (2.4)
  • Calculus & Optimization (2.5)
  • Big O (3.1)
  • Probability (3.2)

⌨️ Part 5: Statistics (4:44:03)

  • Welcome (1.1)
  • Exploration Overview (2.1)
  • Exploratory Graphics (2.2)
  • Exploratory Statistics (2.3)
  • Descriptive Statistics (2.4)
  • Inferential Statistics (3.1)
  • Hypothesis Testing (3.2)
  • Estimation (3.3)
  • Estimators (4.1)
  • Measures of Fit (4.2)
  • Feature Selection (4.3)
  • Problems in Modeling (4.4)
  • Model Validation (4.5)
  • DIY (4.6)
  • Next Step (5.1)

📺 The video in this post was made by freeCodeCamp.org
The origin of the article: https://www.youtube.com/watch?v=ua-CiDNNj30&list=PLWKjhJtqVAblfum5WiQblKPwIbqYXkDoC&index=7
🔺 DISCLAIMER: The article is for information sharing. The content of this video is solely the opinions of the speaker who is not a licensed financial advisor or registered investment advisor. Not investment advice or legal advice.
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Data Science with Python Certification Training in Chennai

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