About This Course
Data science with Python has become a great trend in computational and predictive statistical analysis in recent days. It is used by various organizations to make data-driven decisions. With all the challenges faced, Python has become an indispensable tool for the data science analyst and an important tool for any data scientist. We Offers a comprehensive and yet affordable program in data science. the program has an employment oriented approach and is based on a detailed research of companies’ requirements. it prepares learners for roles like business analyst, data analyst and data scientist.
I m fresher in data science. Is this course suitable for me?
The program is designed to suit all levels of Data Science expertise. From the fundamentals to the advanced concepts in Data Science, the course covers everything you need to know, whether you’re a novice or an expert.
Roles & Responsibilities of Data Scientist
- Fetching data that is relevant to the business from among the huge amount of data that is available in the form of Structured as well as Unstructured Data.
- Organize and analyze the data that is extracted from the piles of data.
- Creation of Machine Learning techniques, programs, and tools in order to make sense of the data.
- Perform statistical analysis for relevant data and predict future outcomes from it.
Why is Python considered as the most Popular language to learn Data Science?
- Python is a multi paradigm programming language - this means that the various facets of Python are most suited for the field of Data Science. It is a structured and object oriented programming language that contains several libraries and packages that are useful for the purposes of Data Science.
- The inherent simplicity and readability of Python as a programming language makes it a language that is preferred by data scientists. The huge number of dedicated analytical libraries and packages that are tailor made for use in data science are some of the main reasons why data scientists prefer the use of Python for Data Science projects, as opposed to any other programming language.
Difference between Panda & Numpy
|
Differences |
Pandas |
NumPy |
|
Data input |
Tabular form - CSV or SQL formats |
Numerical data |
|
Main feature |
Helps add, edit, or create columns or rows to the table. |
Helps perform multiple operations on Array. |
|
Building block |
Series which is built off from ndArrays of NumPy. |
ndArrays - Allow mathematical operations to be vectorized and when compared to Python lists, they are stored with much better efficiency. |
|
Ways to access data |
We can use labeled data - integers as well as numbers to label the elements of the series object. |
Only integers are used for labeling the elements. |
How much maths & statistics required in Data Science?
A large part of the job of a data scientist revolves around playing with data which essentially means numbers. For most of the part, these numbers are given in raw and unstructured state. The job of a data scientist is to find patterns and the relationship between them.
Below are some of the topics that you need to master in mathematics:
- Regression
- Linear Algebra
- Series, sums, and inequalities
- Real and complex numbers and their properties
- Probability
Below are some of the topics that are must in statistics:
- Data summaries, statistics, variance, correlations, and covariance
- Probability distribution functions.
- Sampling, measurements, and error
- Constructing and testing a hypothesis.
What kind of company deploy Data scientist?
We live in a world of data. Your medical diagnosis is data, your investment in the stock market is data, your browsing history is data and so on. Most companies collect data for their own benefit and these data tend to improve our customer experience also. The data science job offered by companies determines what kind of companies they are:
- Small companies use Google Analytics for their analysis as they have fewer resources and fewer data to work with.
- Mid-size companies have data but would need someone to apply ML techniques on it to leverage it.
- Big companies already have teams of data scientists, so they would be needing a new data scientist with specialization. For eg: Visualization, ML expert etc.
Salary Trend
- Data Science has bagged the top spot in LinkedIn’s Emerging Jobs Report for the last three years.
- Thousands of companies need team members who can transform data sets into strategic forecasts.
- Acquire in-demand data science and Python skills and meet that need.
- Data Science and AI have taken the centre-stage as more and more brands realise the possibilities of these tools in the post-COVID world.
- The demand for data engineers was up 50% and the demand for data scientists was up 32% in 2020 compared to the prior year.
- The average salary for a data scientist in the U.S. is $122,338 per year and according to the Bureau of Labor Statistics, the demand for data scientists is pegged to grow by 16% between 2020 and 2028, a rate that’s faster than the average for all occupations.
- Capitalize on the demand for the ‘hottest job of the 21st century’ with a program primed for industry relevance by Harvard.
Learning Objectives
Target Audience
- Participants at all levels of experience
- Analytics professionals willing to work with Python, Software, and IT professionals interested in the field of analytics,
- Anyone with a genuine interest in Data Science.
- Programmers, Developers, Technical Leads, Architects
- Analytics Managers who are leading a team of analysts
- Information Architects who want to gain expertise in
- Predictive Analytics
- 'Python' professionals who want to design automatic predictive models
Curriculum
Data Science Tools
Data Science Platform
Challenges of Deploying Data Science Tools
Considerations for Data Science Tools
Data Science Workflow
Data Science Analytic Tools
Data Science Visualization Tools
Data Science Database Tools
Benefits of Deploying Cloud-Based Tools
Challenges of Deploying Cloud-Based Tools
DevOps for Data Science
Practical Exercise
What is DevOps
Data Science Fundamentals
Data Engineering Fundamentals
Introduction to NumPy for Multi-dimentional Data
Advanced Operations with NumPy Arrays
Introduction to Pandas
Manipulating & Analyzing Data in DataFrames
Machine Learning & Data Analytics
Data Visualization Using Seaborn
