Data Science using Python

Pre-Requisites: There is no pre-requisite as such. Anyone with an aptitude for learning programming and interest for doing analysis on data can be the good fit for this course. The course will use python programming language for doing data analysis so python programming language will be taught as part of this course.

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Key Features

  • Online live classroom available
  • Quality learning materials
  • Small Class Sizes
  • State of the Art Facility
  • Free Retakes
  • Instructor Led Classroom training
  • Certified Industry Experienced Teachers
  • 100% Job Placement assistance

Individuals looking for a career in programming or are currently working as developers, programmers, or web developers should attend this course. The course covers basic data science along with all advance features that an individual will perform as a data science professional. So, this course will help IT Developer, Project Manager and Analytics Professional to grow in their analytics journey.

  • Understand the basic concepts of data science.
  • Understand the tools used for data science and use those tools during the course.
  • Understand data science methodology.
  • Understand databases and SQL concepts used in data science.
  • Use Python programming for data analysis, data visualization & machine learning in data science.
  • Understand the concept and usage of different machine learning algorithms using Python.
  • Use your concept of course understanding in capstone project using Python.

Data science jobs are the most demanding jobs in the Information Technology field today. Prospective job seekers have numerous opportunities. It is the fastest-growing job on LinkedIn and is predicted to create 11.5 million jobs by 2026. This makes Data Science a highly employable job sector.

Data Science is a vastly abundant field and has a lot of opportunities. The field of Data Science is high in demand but low in supply of Data Scientists.

Data Science is one of the most highly paid jobs. According to Glassdoor, Data scientists make an average of $116,100 per year. This makes Data Science a highly lucrative career option.

There are numerous applications of Data Science. It is widely used in health-care, banking, consultancy services, and e-commerce industries. Data Science is a very versatile field. Therefore, you will have the opportunity to work in various fields.

Companies require skilled Data Scientists to process and analyze their data. They not only analyze the data but also improve its quality. Therefore, Data Science deals with enriching data and making it better for their company.

Data Scientists allow companies to make smarter business decisions. Companies rely on Data Scientists and use their expertise to provide better results to their clients. This gives Data Scientists an important position in the company.

In this course, you will learn about how the data is used with different analytics steps such as business understanding, data collection, data wrangling, data exploration, data modeling and data visualization etc.

What Is a Data Science?

  • What is Data Science
  • Fundamentals of Data Science
  • What is big data, How Big Data is Driving Digital Transformation
  • What Makes Someone a Data Scientist?
  • Different roles in data science
  • Applications of Data Science

Tools for Data Science

  • Jupyter Notebook for Python

Data Science Methodology

  • Introduction to Data Science Methodology
  • Problem statement/Business Understanding
  • Analytic Approach
  • Data requirements
  • Data Collection
  • Data Understanding
  • Data Preparation
  • Modeling
  • Evaluation
  • Deployment
  • Feedback

Python Programming for Data Science

  • Python Basics
  • Python Data Structures
  • Python Programming Fundamentals
  • Working with data in Python
  • Analyzing sample data in Python

Databases and SQL for Data Science

  • Introduction to Databases & Basic SQL concepts
  • Advanced SQL
  • Accessing databases using Python
  • Sample database access using Python

Data Analysis using Python

  • Importing Datasets
  • Data Wrangling
  • Exploratory Data Analysis
  • Model Development
  • Model Evaluation
  • Assignment covering the above topics

Data Visualization with Python

  • Introduction to data visualization tools
  • Basic and Specialized Visualization Tools
  • Advanced Visualizations in Python

Machine Learning with Python

  • Introduction to Machine Learning
  • Regression
  • Classification
  • Clustering
  • Recommender Systems
  • Assignment on Machine Learning in Python

Capstone project covering the following

  • Requirement understanding/ Problem Statement
  • Data collection
  • Data scrubbing
  • Data exploration
  • Data Modeling
  • Data Visualization
  • Story telling

What is Data Science with Python Course?

This course focuses on the practice of data analytics, the role of the Data Scientist, the main phases of the Data Analytics Lifecycle, analyzing and exploring data with python, statistics for model building and evaluation, the theory and methods of advanced analytics and statistical modeling, the technology and tools that can be used for advanced analytics, operational zing an analytics project, and data visualization techniques.

Why Data Science with Python Course?

Data science jobs are the most demanding jobs in the Information Technology field today. Prospective job seekers have numerous opportunities. It is the fastest-growing job on LinkedIn and is predicted to create 11.5 million jobs by 2026. Data Science is one of the most highly paid jobs. According to Glassdoor, Data scientists make an average of $116,100 per year. There are numerous applications of Data Science. It is widely used in health-care, banking, consultancy services, and e-commerce industries. Data Science is a very versatile field. Therefore, you will have the opportunity to work in various fields as a highly lucrative career option.

Who should go for this Training?

Individuals looking for a career in programming or are currently working as developers, programmers, or web developers should attend this course. The course covers basic data science along with all advance features that an individual will perform as a data science professional. So, this course will help IT Developer, Project Manager and Analytics Professional to grow in their analytics journey.

What background knowledge is necessary?

Not required as such. Anyone with an aptitude for learning to program and has an interest in analyzing data can be a good fit for this course. The course will use python programming language for doing data analysis so python programming language will be taught as part of this course.

What will I learn in this course?

  • Understand the basic concepts of data science.
  • Understand the tools used for data science and use those tools during the course
  • Understand data science methodology
  • Understand databases and SQL concepts used in data science
  • Use Python programming for data analysis, data visualization & machine learning in data science
  • Understand the concept and usage of different machine learning algorithms using Python
  • Use your concept of course understanding in capstone project using Python

What is the duration of this Course?

Total Duration is 48 hours

How much does a Data Scientist earn?

Salary Estimates as on October 8th, 2020 in for a Data Scientist in USA are

Zip Recruiter – 76K-160K per Year

Glass Door – 83K-150K per Year

PaySclae USA – 67K-130K per Year

The average salary for a Data Scientist in Detroit, Michigan is $87815 as per PaySclae USA.

What are the prerequisites for this course?

There is no pre-requisite as such. Anyone with an aptitude for learning programming and interest for doing analysis on data can be the good fit for this course. The course will use python programming language for doing data analysis so python programming language will be taught as part of this course.

What skills do I learn in this course – Data Science with Python?

Here are few to mention the practice of data analytics, the role of the Data Scientist, the main phases of the Data Analytics Lifecycle, analyzing and exploring data with python, statistics for model building and evaluation using the following tools and methods.

  • Jupyter Notebook for Python
  • Python Programming for Data Science
  • Databases and SQL for Data Science
  • Machine Learning with Python
  • Deep Learning

How do I become a Data Scientist?

This course is designed to give you an insight into Industry driven Data Science tools and methodologies, which is enough to prepare you to excel in your next role as a Data Scientist. The program will train you on Python, Machine Learning techniques, data reprocessing, regression, clustering, data analytics, statistics for model building and evaluation, the theory and methods of advanced analytics and statistical modeling, the technology and tools that can be used for advanced analytics, operational zing an analytics project, and data visualization techniques.

What software/technology stacks do you use?

While we do work in Python during the training, knowledge of any programming language will work- as we teach the principles from a “software agnostic” point of view, the principles transfer across programming languages. We teach how to interpret data, and then how to apply machine learning to take that to the next level.

How much statistics will I need to know?

We work hard to ensure that no prior statistics knowledge is required. We will teach you all the basics you need to know before and during the bootcamps. We cover correlations, hypothesis testing, and Linear Regression in the Course, all at a level appropriate for someone with no/little statistics experience.

What is the difference between a Data Scientist, Data Analyst, and Data Engineer?

We’ve certainly seen variation in regards to what employers have in mind when they use these terms, so please consider the answers below as general guidelines.

A Data Analyst is someone who creates and communicates insights from data to measure outcomes, make predictions, and guide business decisions. Often, there is a lighter coding burden placed upon someone with the title Data Analyst, though they may be expected to know certain languages or packages in R or python.

A Data Engineer is the designer, builder, and manager of the information or “big data” infrastructure. Each develops the architecture that helps analyze and process data in the way the organization needs it – and they make sure those systems are performing smoothly.

The term Data Scientist is used the most broadly. A job posting for a Data Scientist might describe a role identical to others calling for “data analyst,” though there is usually more diverse coding skills needed for a data scientist job. For the most part, data scientists are asked to participate in the entire cycle of problems and solutions. They help identify opportunities for companies to use data, while also finding, collecting, and integrating relevant data sources, performing analyses of varying degrees of complexity, writing code and creating tools that teams and businesses can use over time, and telling the story of what they’ve done to company stakeholders.

What is the Future of Data Science?

Putting it slightly differently – Data Science is the future. No businesses or industries for that matter will be able to keep up without data science. A large number of transitions have already happened worldwide where businesses are seeking more data-driven decisions, more is to follow suit. Data science quite rightly has been dubbed as the oil of the 21st century which can mean endless possibilities across industries. So, if you are keen on pursuing this path, your efforts will be highly rewarded with not just a fulfilling career and fat pay cheques but also a lot of job security.

Over the last few years, data science has continued to evolve and permeate nearly every industry that generates or relies on data. In a 2010 article published in The Economist, Kenneth Cukier says data scientists “combine the skills of software programmer, statistician, and storyteller/artist to extract the nuggets of gold hidden under mountains of data.” Google Chief Economist Hal Varian told the McKinsey Quarterly that he was concerned with the deficit of individuals qualified to analyze the “free and ubiquitous data” being generated. He said, “The complimentary scarce factor is the ability to understand that data and extract value from it. I do think those skills, of being able to access, understand, and communicate the insights you get from the data analysis are going to be extremely important.”

What are the applications of Data Science?

Here are few areas where Data Science plays key role:

  • Product Recommendation
  • Future Forecasting
  • Fraud and Risk Detection
  • Self-Driving Car
  • Image Recognition
  • Speech to text Convert

What are Jobs in Data Science?

Here are different job profiles that can eventually lead you to become a data scientist

  • Data Analyst
  • Data Engineers
  • Database Administrator
  • Machine Learning Engineer
  • Data Architect
  • Statistician
  • Business Analyst
  • Data and Analytics Manager
  • Data Scientist

Course Number : DATA-0-101

Duration : 48 hours

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