Data Science with Python, Machine Learning & AI Professional

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 modelling, the technology and tools that can be used for advanced analytics, operational zing an analytics project, and data visualization techniques, ETL & Feature creation, Model definition and Training, Model Evaluation, Model Tuning, Model Deployment, Documentation, Loading Data, Data preparation with Keras, Linear Classifier PyTorch, Building a classifier with Pre-Trained Model, Evaluating and Testing Pre-Trained models and Three Capstone Projects.

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.

Data Science has helped various industries to automate redundant tasks. Companies are using historical data to train machines in order to perform repetitive tasks. This has simplified the arduous jobs undertaken by humans before.

Data Science involves the usage of Machine Learning which has enabled industries to create better products tailored specifically for customer experiences.

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 Data Science with Python, Machine Learning & AI Professional 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, Machine Learning & AI Professional 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, Machine Learning with Python, Deep Learning A.I 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 programming and has interest for doing analysis on 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?

This Course consists of 3 Modules

Module 1: Data Science using Python
Module 2: Machine Learning using Python
Module 3: Deep Learning & Artificial Intelligence

What is the duration of this Course?

Total Duration is 144 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, Machine Learning & AI Professional?

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.

Is R used extensively today in data science?

We would say that R was probably THE language for doing statistics or “data science” work about 5-10 years ago. Today, as the Python sci-stack caught up and keeps growing, it’s about as widely used as Python for similar tasks. I can see a shift more towards Python in future though because there seems to be more development going on at the moment towards scalability and computational efficiency.

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.