Data Science Bootcamp with Computer Programming Language

Hello, everyone, I am thrilled to help you unleash your full potentials and help you with your dreams of becoming a Data Scientist. Are you interested in Data Science? Isn’t too late to learn Data Science from the Scratch? Well, if you take further education in a university, it might be a waste of time and money because the moment you graduate, the tools being used and discussed might already be obsolete when you graduate.

If so, then you'll need to have a strong foundation in Statistics, Data Science, and Computer Programming. Our Data Science Bootcamp Course with Computer Programming Language will teach you everything you need to know to succeed in this exciting and in-demand field.

Our bootcamp course is designed for aspiring Data Scientists, Data Analysts, and Statisticians. No prior experience is required. Even if you are coming from different field, this course is fit for you. We'll provide you with everything you need to succeed in this field. From concept to theoretical and practical hands-on experience and applications will be covered in this course.

To make sense of the data, then you need to understand what to do with the data, how to process the data and how to interpret the data. To help you out digging the data, you’ll need to learn Data Science programming tools. And that’s the purpose of this course.

We will cover the basic of Statistics and basic concept in Data Science as well as Business Management. After completing our bootcamp course, you'll be well-prepared for a career in data science. You'll have the skills and knowledge to:

1. Analyze data to identify trends and patterns.

2. Build and deploy machine learning models.

3. Communicate your findings to stakeholders.

4. Work with other professionals to solve complex problems.

Don't miss your chance to learn the skills you need to succeed in data science. Enroll in our Data Science Bootcamp with Computer Programming Language today!

The demand for data professionals is high, but the supply is low. The Bureau of Labor Statistics projects that employment of data scientists will grow 28% from 2020 to 2030, much faster than the average for all occupations. This is due to the increasing use of data in businesses and organizations across all industries.

There is a gap between the skills that data professionals need and the skills that they have. Many data professionals have a strong background in computer science or statistics, but they lack the skills in the other discipline. This can make it difficult for them to collaborate with colleagues and to effectively use data to solve business problems.

There is a lack of training programs that bridge the gap between computer science and statistics. Many universities offer programs in computer science or statistics, but few offer programs that combine the two disciplines. This makes it difficult for data professionals to get the training they need to be successful in their careers.

This course provides a solution to the gap between computer science and statistics by providing students with the skills they need to be successful data professionals. The course covers topics in both computer science and statistics, and it provides students with the opportunity to apply their skills to real-world problems.

By taking this course, students will be able to:

1. Understand the principles of data science

2. Use programming languages to analyze data

3. Communicate the results of data analysis

4. Work effectively with colleagues from different disciplines

This course is essential for anyone who wants to pursue a career in data science. It will provide you with the skills you need to be successful in this growing field.

Here are some additional challenges that data professionals face:

1. Data privacy and security. Data professionals need to be aware of the ethical and legal implications of working with data. They also need to take steps to protect data from unauthorized access.

2. The ever-changing landscape of data science. The field of data science is constantly evolving. Data professionals need to be able to keep up with the latest trends and technologies.

3. The need for creativity and problem-solving skills. Data science is not just about crunching numbers. It also requires creativity and problem-solving skills. Data professionals need to be able to come up with new ideas and solutions to problems.

Despite these challenges, the field of data science is a rewarding one. Data professionals have the opportunity to make a real impact on the world by using data to solve problems and make better decisions.

Benefits of Enrolling in Our Data Science Bootcamp

1. Learn from experienced instructors who are experts in data science.

2. Complete a comprehensive curriculum that covers all the essential topics.

3. Gain hands-on experience with data science tools and techniques.

4. Build a strong network of fellow students and alumni.

Why Choose Us?

We have a proven track record of success. Our alumni have gone on to successful careers in data science, data analysis, and statistics.

We offer a flexible learning format. You can choose to attend our bootcamp full-time or part-time.

The Relevance of Statistics in Data Science

Data science is a rapidly growing field that is changing the way businesses operate. Data scientists use their skills in mathematics, statistics, and programming to collect, clean, analyze, and interpret large amounts of data. This data can be used to make better business decisions, improve customer service, and develop new products and services.

Statistics is a fundamental part of data science. It provides the foundation for understanding and analyzing data. Statistical concepts such as probability, hypothesis testing, and regression analysis are essential for data scientists.

Here are some of the ways that statistics is used in data science:

• Data collection: Data scientists use statistics to design surveys and experiments to collect data. They also use statistics to clean and organize data.

• Data analysis: Data scientists use statistics to analyze data to identify trends, patterns, and relationships. They also use statistics to test hypotheses and make predictions.

• Data visualization: Data scientists use statistics to create visualizations of data. These visualizations can be used to communicate the results of data analysis to stakeholders.

Statistics is a powerful tool that can be used to make sense of data. Data scientists who have a strong understanding of statistics are in high demand.

Here are some of the benefits of using statistics in data science:

• Improved decision-making: Data scientists can use statistics to help businesses make better decisions. For example, data scientists can use statistics to predict customer behavior, forecast sales, and optimize marketing campaigns.

• Increased customer satisfaction: Data scientists can use statistics to improve customer service. For example, data scientists can use statistics to identify customer pain points and develop solutions to improve the customer experience.

• New product development: Data scientists can use statistics to help businesses develop new products and services. For example, data scientists can use statistics to identify new market opportunities and test new product concepts.

If you are interested in a career in data science, it is important to develop your skills in statistics. There are many online courses and resources available to help you learn statistics. With hard work and dedication, you can develop the skills you need to become a data scientist.

Here are some additional tips for learning statistics:

• Take an online course: There are many online courses that can teach you the basics of statistics.

• Read books and articles: There are many books and articles that can teach you more advanced statistics concepts.

• Practice, practice, practice: The best way to learn statistics is by practicing. Try to solve different statistics problems.

With hard work and dedication, you can learn statistics and become a data scientist.

Relevance of Computer Programming Language in Data Science

Data science is a rapidly growing field that is changing the way we live and work. Data scientists are in high demand, and they use a variety of programming languages to analyze data and build models.

Some of the most popular programming languages for data science include R, Python, and SAS.

1. R is a free and open-source programming language that is specifically designed for statistical computing and graphics. R is widely used by statisticians, data scientists, and researchers.

2. Python is a general-purpose programming language that is also popular for data science. Python is known for its simplicity and readability, and it has a large community of users and developers.

3. SAS is a commercial programming language that is used by businesses and organizations for data analysis. SAS is known for its stability and performance, and it offers a wide range of features for data management, statistical analysis, and reporting.

In addition to these three languages, there are many other programming languages that can be used for data science. Some of the most popular alternatives include:

1. Julia is a high-performance programming language that is designed for numerical computing.

2. MATLAB is a commercial programming language that is used for mathematical computing and visualization.

3. Octave is a free and open-source programming language that is similar to MATLAB.

The choice of programming language for data science depends on a variety of factors, such as the specific tasks that need to be performed, the environment in which the code will be run, and the preferences of the data scientist.

However, all of the programming languages mentioned above are powerful tools that can be used to analyze data and build models. If you are interested in a career in data science, it is important to learn one or more of these languages.

Here are some of the reasons why programming languages are relevant in data science:

1. Data science is a data-driven field. Data scientists need to be able to collect, clean, and analyze data. Programming languages provide the tools that data scientists need to do this.

2. Data science is a computational field. Data scientists need to be able to build models and algorithms that can be used to make predictions. Programming languages provide the tools that data scientists need to do this.

3. Data science is a collaborative field. Data scientists often work with other professionals, such as statisticians, business analysts, and engineers. Programming languages provide the tools that data scientists need to communicate with these other professionals.

If you are interested in a career in data science, it is important to learn one or more of the programming languages that are relevant to this field.

I am proud to offer you the most advanced and comprehensive course on Data Science.

Python is a Computer Programming Language which was written and introduced in 1990s by Guido Van Rossum.

Guido Van Rossum is a Dutch Programmer was born in Haarlem, Netherlands on January 31, 1956. He has a master’s degree in Mathematics and Computer Science from the University of Amsterdam in 1982.

Python's Visibility

With its unprecedented power and usefulness, Python has become of the hottest and fastest growing programming language since it was launched and is become one of the buzzwords in Digital Transformations together with other domains in the Pantheon of Technology – Data Science and Big Data.

Data Science is a multi-disciplinary skill in Statistics/ Data Mining, Computer Science and Business Management. Data Science was regarded as the solution to provide scalable insights based on Big Data.

The advancement in Technology makes these domains and areas visible to almost all big industries today. Big enterprises invested huge amount of money to install Data Science team in their organization to take care and manage Big Data infrastructures and architecture.

Prior to Pandemic Outbreak, these companies are continuously reaping success and not being swayed down because of the successful deployment of Artificial Intelligence Technology, Machine Learning, Automation, Internet of Things (IoT), Blockchain and other areas of Data Science.

Skills in Statistics, Data Mining or Data Wrangling was successfully used and applied to these companies and areas because of the Computer Programming Language such as Python. Business Models was carefully curated by experts in Business Management. Thus, Data Science was regarded as the sexiest job in the 20th Century because it encapsulated a wide-spectrum of knowledge, skills and expertise.

Based on Stack Overflow Developer’s Survey in 2019, Python was regarded as the second and the most preferred language with 73% of the developers are choosing it over other programming languages and is expected to dominate the Marketplace for Programming Languages.

Why Do We Need To Learn Python?

Python is very useful in general aside from being an open-source programming language. It was used by Amazon, Google, Reddit, Instagram, and all other big companies around the world.

• Python is user-friendly programming language – Python was able to simplify coding process just like transforming complicated coding to make it simpler and more straightforward. This what makes Python easy to learn and understand that even kids can able to understand and use.

  
  

• Python makes us more Productive – Instead of us taking further education to know the old school programming languages, Python makes everything simple. It makes the Programming task a lot easier compared with other programming languages.

  
  

• Python is very Dangerous in a Positive way – It can used for anything. It is very powerful as you can generate insights based from actual sentiments of the people, It can replicate things through machine learning algorithms, It can automate things through automation, It can replicate human through the development of virtual agents and the like. Because of its powers, it comes with great responsibility. It can be dangerous if you will use its powers to connive with evil who have a lot of destruction activities which includes all forms of fraud.

  
  

• Python is a language for creating a script – You can directly type your script to its interpretation environment such as IDLE and many other. It does not require compilation like any other languages. You can easily detect and identify errors in your scripts. This makes a programming a fan activity.

  
  

• Python is a Cross-Platform Programming Language – Anyone can use it as long as you have the motivation and purpose of using it. If you are in non-analytics field but you want to learn this because you have a goal and motivation, you can easily use it. You can use and install Python on Windows, Mac, Linux and from other platform like Raspberry Pi. You can also run Python on Android and IOS tablets.

  
  

• Python uses dynamic typing of variables – When you start programming, you do not actually need to explain the machine what the variables is supposed to be. You can just write your variables as it is.

  
  

• Python is very collaborative language – there are so many experts have written libraries. You do not need to build and create your own library as there are available libraries in place. All you can do is to install.

  
  

• Python is Open-Source Programming Language – It does not require you to pay for licenses. You can download and run python from different distribution channels like such as Anaconda.

Where to use Python

Python can be use everywhere and anywhere. You can use Python to whatever you plan to do or interested to work on.

• In Space – Python was used for the Central Command System at the International Space Station’s Robonaut 2. The European Mission to Mars was planning to use Python to collect and study soil samples.

  
  

• In laboratories – Python was used to generate insights from the atom smashing experiments at the CERN Large Hadron Collider.

  
  

• In Astronomy – Python was used to control and monitor system of the MeerKat Radio Telescope Array.

  
  

• In Movie Studio – The Star Wars experts uses Python to automate movie productions. Effects Software’s computer-generated imagery program Houdini uses Python for the Programming interface and to script the engine.

  
  

• In Games – Activision uses Python for building games, testing, and analyzing stuffs. They are also using Python to detect people cheating in game activity.

  
  

• In Video and Music Industry – Spotify, Netflix, and other streaming services uses Python for the recommendation engine. It understand the people’s preference when it comes to music, movies and therefore, automatically generate recommendations.

  
  

• In Search Engine – Google uses Python all over in its early development stage.

  
  

• In Medicine – Other Medical Institution, Drugs Manufacturing company uses Python to develop medicine for illnesses such as Cancer.

  
  

• In Virtual Agents and Robots – Python was used to developed applications for robots.

  
  

• Internet-of-things(IoT) – you can use python to developed application for automation and for other integrated systems in the Internet-of-things.

Data Science as I always define is not about writing a very complicated script or code.

Data Science is making use of data to generate insights which provides an impact and value for a company. Value could be in the form of a products that can offer by the company to their stakeholders in exchange of a revenue, it could be in the form of a products that will help improve Business processes. It could be in the form of a product recommendations for the company.

Data is an essential driver for growth and transformation initiative of every organizations; thus, it is a must to build and develop framework and infrastructure to organize and manage data. To effectively handle and manage Big Data, they will be needing tools such as MapReduce, Hadoop, Spark, and many others. One must need someone who understand the engineering and architecture of Big Data, thus the rise of Data Architect and Data Engineer.

To make these products come true, one need someone who can confidently used tools to build an advanced machine learning models, or artificial intelligence algorithms, someone who have a great level of creativeness to visualize the insights, or someone who have a very outstanding skills in Writing Code.

But the real sense of a Data Scientist is someone who can generate insights and recommendations to solve real company problems using data and it doesn’t care or need that one must be good in advanced analytics tools, programming tools, and big data infrastructure and architecture like R, SAS, Python, Tableau, D3, Qliview, Sisense, Scala, MapReduce, Hadoop, Cloudera and all others.

This is a huge misconception about data science especially on YouTube and other search engines and the reason was because of the differentiation between the popularity of certain domains of expertise such as Data Science and what is really needed in the industry.

Enterprises begun to build infrastructure to support the handling of the data and to do that, they will need a computing technology like MapReduce, Hadoop, Spark thus the rise of Big Data in 2020 sparked the rise of Data Science to support the needs of the businesses and enterprises to draw insights from the massive types of datasets from structured to non-structured datasets.

The journal of Data Science described this domain of skills as almost everything that has something to do with data collection, analysis and modelling and the most important part of Data Science that made it into the mainstream and the Pantheon of Technology is the applications of Artificial Intelligence, Machine Learning Algorithms, Automation, IoT, etc. With the skills associated to Data Science, enterprises are very confident that it is possible to train computers with a data driven approach rather than a knowledge approach.

The Rise of Big Data

As we all know, there are 2.5 quintillion bytes of data are generated every seconds of the day. Can you imagine how can you make use of data if you do not have the architecture and infrastructure systems that organize and manage big data.

Big Data is one of the buzzwords today because of its impact to organizations and enterprises. It has a lot of benefits for every organization. Every companies around the world are talking about it. Every organizations are taking their steps to explore and to manage big data.

But what Big Data really is? How is it changing the way enterprises and all organization around the world are trying to make use of Big Data. Where are these data coming from and how to process it and how to make use it?

What is Big Data?

In this lesson, I will be going to define and explain what all about Big Data is.

Competition in Business was very tough and the only thing to position their business in the market is to get something from the data. Making use of data is an effective strategy to get into the mainstream. In a pharmaceutical and medicine manufacturing company, they need data for them to develop medicine and for them to determine the mixtures needed to make the medicine effective to cure certain illness. To understand the people’s buying preferences in a car manufacturing company, they will be needing data for them to target the needs of the buyers and therefore they can manufacture and build cars based on the buyer’s preferences. Big Data also plays a big role in FMCG company, they will be needing customers buying behaviors, needs and other exogenous factors.

Small and big enterprises are triggered to make use of the data that is why they were challenge on where to store, how to organize and how to process data. To serve their purpose, enterprises started to build and develop framework and system to manage, store and process data. These are the main components of management architecture in every organizations.

As the Technology is continuously evolving and improving, this shed light to more and more enterprises to explore and learn more about data. Authors and luminaries have shared their views about the definition of Big Data.

Definition of Big Data

They defined Big Data based on the three attributes and characteristics, namely: Volume, Veracity and Variety. More recently, luminaries have suggested the definitions of Big Data based on Veracity, Value and Variability.

The idea of Big Data has been around for long years, but it was just gained momentum in the buzzword in the early 2000 when the Doug Laney – A well-known analyst articulated the definition as the three V’s such as Volume, Velocity and Variety.

Volume: Enterprises and organization collect data from different sources like internet, business transactions, social media, devices, and more other sources. Data lakes and Hadoop have eased the burden of storing this volume of data. As of this era of technology, there are more than 2.5 quintillion bytes of data are generated every day and the only platform to store this Big Data are the Data Lake, and the most known platform – Hadoop.

Velocity: They also characterized Big Data based on the unprecedented speed in data streaming. Enterprises become interested to understand data influx in a timely or real-time manner. This is when RFID tags, Sensors, Meters, IoT has been introduced. For example, you have an IoT device installed in your home and you want your appliances, doors, aircon to connect to a certain device and with just your voice command, you can able to say, “Turn On lights, open the door, turn-off the aircon, etc. How come these devices worked by just your voice command. Can you imagine the speed of the data to follow your order? Another example is the RFID, that when you are in the tollgate, the bar will automatically open as you enter to the sensor area. How come the sensor easily detect the approaching car.

Variety: it describes data based on its formats from being structured, semi-structured and Un-structured.

Veracity of the data comes into the pictures because of its quality. Since data are coming from different sources, it is difficult to just use it and rely to the data being stored in the databases. Thus, veracity comes in to answer data integrity. Enterprises and organizations still need to match, inspect, clean, and transform data in such a way that it will give sound insights to the business.

While there are no hard, concrete, and fast rules about the size a database needs to have for the data to store and consider it as “big”. Instead, what typically defines big data is the need for new techniques and technologies and tools to be able to process it and make use of it.

To use big data, we need programs that span a physical and/or virtual machine working together in concert to process all the data in very short or span period. We must make use of data in an agile situation.

Installing software and applications on multiple machines to work together in an efficient way so that each applications and software knows which components and attributes of the data to process, and then being able to put the results from all the machines together to make sense of a large volume of data, takes an advanced and seamless programming tools and techniques. Since it is typically faster for programs to access data stored locally instead of over a network, the distribution of data across a cluster and how those machines are networked together are also important when thinking about big data.

How To Make Use of Big Data

One of the best and the most useful tool to process and analyze big data to produce information which yield meaningful insights is the MapReduce. MapReduce is a processing technique and a program model for distributed computing based on java. The MapReduce algorithm contains two important tasks, namely Map and Reduce. Map takes a set of data and converts it into another set of data, where individual elements are broken down into tuples (key/value pairs). The Reduce function provides a summary of this data by combining it all together. While largely credited to research that took place at Google, MapReduce is now a generic term and refers to a general model used by many technologies.

MapReduce is a method for taking a large data set and performing computations on it across multiple computers, in parallel. It serves as a model for how to program and is often used to refer to the actual implementation of this model.

What tools are needed to analyze and make use of big data?

To analyze and make use of Big Data, there is a need to use tool such as Apache Hadoop. Apache Hadoop is a collection of open-source software utilities that facilitate using a network of many computers to solve problems involving massive amounts of data and computation. It provides a software framework for distributed storage and processing of big data using the MapReduce programming model.

The Apache™ Hadoop® project develops open-source software for reliable, scalable, distributed computing.

The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.

Apache Hadoop is a framework for storing and processing data at a large scale, and it is completely open source. Hadoop can run on commodity hardware, making it easy to use with an existing data center, or even to conduct analysis in the cloud. Hadoop is broken into five main parts or modules:

1. Hadoop Common: The common utilities that support the other Hadoop modules.

2. Hadoop Distributed File System (HDFS™): A distributed file system that provides high-throughput access to application data.

3. Hadoop YARN: A framework for job scheduling and cluster resource management.

4. Hadoop MapReduce: A YARN-based system for parallel processing of large data sets.

5. Hadoop Ozone: An object store for Hadoop

Other Tools Used to Analyze Big Data

Other tools are out there too. One that receives a lot of attention is Apache Spark. The main selling point of Spark is that it stores much of the data for processing in memory, as opposed to on disk, which for certain kinds of analysis can be much faster. Depending on the operation, analysts may see results a hundred times faster or more. Spark can use HDFS, but it is also capable of working with other data stores, like Apache Cassandra or OpenStack Swift. It's also fairly easy to run Spark on a single local machine, making testing and development easier.

Other big data tools

Of course, these are not the only big data tools out there. There are countless open source solutions for working with big data, many of them specialized for providing optimal features and performance for a specific niche or for specific hardware configurations.

The Apache Software Foundation (ASF) supports many of these big data projects. Here are some that you may find useful.

1. Apache Beam is "a unified model for defining both batch and streaming data-parallel processing pipelines." It allows developers to write code that works across multiple processing engines.

2. Apache Hive is a data warehouse built on Hadoop. A top-level Apache project, it "facilitates reading, writing, and managing large datasets … using SQL."

3. Apache Impala is an SQL query engine that runs on Hadoop. It's incubating within Apache and is touted for improving SQL query performance while offering a familiar interface.

4. Apache Kafka allows users to publish and subscribe to real-time data feeds. It aims to bring the reliability of other messaging systems to streaming data.

5. Apache Lucene is a full-text indexing and search software library that can be used for recommendation engines. It is also the basis for many other search projects, including Solr and Elasticsearch.

6. Apache Pig is a platform for analyzing large datasets that runs on Hadoop. Yahoo, which developed it to do MapReduce jobs on large datasets, contributed it to the ASF in 2007.

7. Apache Solr is an enterprise search platform built upon Lucene.

8. Apache Zeppelin is an incubating project that enables interactive data analytics with SQL and other programming languages.

Other open source big data tools you may want to investigate include:

1. Elasticsearch is another enterprise search engine based on Lucene. It's part of the Elastic stack (formerly known as the ELK stack for its components: Elasticsearch, Kibana, and Logstash) that generates insights from structured and unstructured data.

2.  Cruise Control was developed by LinkedIn to run Apache Kafka clusters at large scale.

3. TensorFlow is a software library for machine learning that has grown rapidly since Google open sourced it in late 2015. It's been praised for "democratizing" machine learning because of its ease-of-use.

As big data continues to grow and importance, the list of open source tools for working with it will certainly continue to grow as well.

Hello Everyone, this is Vincent Torre Bongolan.

I am very proud and excited to inform you about this complete python programming bootcamp course necessary for data science and analytics domain.

Welcome to this Complete Python Bootcamp Course for Analytics and Non-analytics professionals.

Long before the Pandemic outbreak, I have been working from home because I was born with Poliomyelitis. New Normal is not actually new normal for me because I have been doing this since before.

Data Science was regarded as the sexiest job in the 20th Century and I can see that in the next 20-30 years, there will be a big leap and explosion of careers in Data Science and analytics, thus I am very excited to invite everyone to enrol this course on Data Science with t he use of Computer Programming Language called Pythons.

I am very excited to help and guide you on your journey to enter the mainstream of Data Science and Analytics. It is your time to shine and maximize the opportunity to learn and upskill yourself.

I encourage the students to read, reflects and do not escape any portion of this lesson for you to be able to get the complete learning experience.

In this course, I will be discussing the foundations of becoming a Data Scientist. This course encapsulates foundations needed in Data Science and Analytics.

I remember way back in 2018 when I took up the same course from an E-Learning platform, it costs me Php.38,000 or equivalent to almost $760 USD for just Python Programming alone. I have no choice during that time because I want to upskill to position myself into the mainstream of Data Science and Analytics Revolutions.

Since then, I keep on improving my skills and competencies in Data Science. This course is also offered to some E-Learning Platforms and some Universities here and abroad.

One thing that differs this course to other courses is that it encapsulates skills and competencies necessary in Data Science and Analytics as it discusses core domains of knowledge from Statistics, Data Analysis, Data Wrangling, Computer Science domain which includes databases, sql and computer programming language using Python and business management domain which includes exercises and capstone projects about data science.

Another good attribute of this course is that it was designed for any type of learner. This course is designed for analytics and non-analytics professionals who wanted to learn more about Data Science and Python Programming Language even though you have no prior programming language experience.

To give you the snapchat of the course, I will cover the following key topics.

Python Programming Language

1.What is Python

2.Setting-up Python

3.Basics of Python

4.Python Data Structures

5.Comparison Operators

6.Python Statements

7.Methods and Functions

8.Lambda Expressions

9.Nested Expressions

10.Object-Oriented Programming

11.Modules and Packages

12.Advanced Python Functions

13.Python’s Libraries

Introduction to Data Science

1.What is Data Science

2.Why we need to learn Data Science

3.Applications of Data Science

4.Tools used in Data Science

5.Databases and SQL for Data Science

6.Data Analysis

7.Data Wrangling

8.Exploratory Data Analysis

9.Model Development

10.Model Evaluation and Refinement

Visualization in Data Science

1.Introduction to Visualization

2.Introduction to Machine Learning

3.Classification algorithm

4.Clustering Algorithm

5.Recommender System

6.Natural Language Processing

Deep Learning

1.What is Deep Learning

2.What is TensorFlow

3.Introduction to Kerras

4.CNN

5.RNN

6.Transfer Learning

7.Hyper Tuning

8.Deep Learning Regularization

There are two ways using which you can install python on windows.

1.  Install python based on shell

2.  Install python using Distribution Platform - Anaconda

    2.1. Jupyter Notebook

    2.2. PyCharm

    2,3. Spyder