Data Science and Machine Learning with Python and Libraries

Learn Data Science and Machine Learning with Python and Libraries such as Numpy, Matplotlib, Pandas and much more!

What you’ll learn

  • Data Science with Python.
  • Using Python Libraries for Data Science.
  • Data Analysis.
  • Python Libraries such as Numpy, Matplotlib, Pandas.
  • Visualizing Data.
  • Linear Algebra.
  • Statistics.
  • Probability.
  • Hypothesis and Inference.
  • Gradient Descent.
  • Machine Learning.
  • k-Nearest Neighbors.
  • Naive Bayes.
  • Simple Linear Regression.
  • Multiple Regression.
  • Logistic Regression.
  • And Much More!.

Course Content

  • Introduction –> 3 lectures • 3min.
  • Visualizing the Data –> 5 lectures • 31min.
  • Linear Algebra –> 3 lectures • 26min.
  • Statistics –> 7 lectures • 37min.
  • Probability –> 7 lectures • 43min.
  • Hypothesis and Inference –> 8 lectures • 49min.
  • Gradient Descent –> 6 lectures • 40min.
  • Working with Data –> 3 lectures • 28min.
  • Machine Learning –> 4 lectures • 32min.
  • k-Nearest Neighbors –> 2 lectures • 16min.
  • Simple Linear Regression –> 3 lectures • 14min.
  • Multiple Regression –> 3 lectures • 12min.

Data Science and Machine Learning with Python and Libraries


  • A little bit of Python understanding will be good (not necessary).




As the world is progressing in science and technology, there is an enormous increase in the need for advanced tools to store information and mine data that is being produced indefinitely. And the key to this problem is data science. Data science is a field of study that develops scientific and systematic methods to record, process and analyze data to withdraw significant and useful information that can be both structured and unstructured. Unstructured data is the one that is generated by mobile devices and websites while structured data is an organized data which is mostly generated by the users e.g. emails, chats, telephone calls etc. Data science uses scientific methods and algorithms to extract knowledge. Industries require the use of this field immensely and the industrialists now realize the value of data science and the benefits it can provide to the business, thus, it has become very popular currently.

The need for data science

An immediate question that rises in the mind after hearing about data science is why is there a need to dig into depths for such a tool? So, it’s important to understand that previously the data produced used to be structured and thus it was somehow easy to extract the meaningful information and process it. However, contemporarily the data that is being produced is mostly unstructured as there are multiple sources of its generation such as multimedia files, logs, documents etc. and data science provides aid in turning raw data into consequential one.

Human brains possess the intelligence to perceive things as they are i.e. processing the information that we see and store it. It is just a power that we humans have and because we are doing it constantly without any deliberate effort, it is trivial to us. However, parallel to this brilliant power that we hold, it is also to have a clear understanding of the fact that it is limited. Human brain is also prone to forgetting, and impeding memory, perceptions and predictions. Here, the computer’s prowess proves to be helpful. With the advance improvement in technology, we are now able to leave our locations through our smartphones for example uber that makes our traveling so much convenient, our movements can be tracked easily, our online behaviors and patterns of search are constantly being recorded through our acceptance of cookies, the steps we take during a day can be tracked by the help of some specific apps, even our health can be tracked. All of this is possible due to the invention of technology, softwares and the ability to record and process information adequately. Our information is being stored and without us even noticing. This tracking ability is not only applicable on the people sitting at home, but government agencies, stock markets, civil officers, intelligence agencies, business owners are all the concerned parties and is useful for each one of the occupations. Data science is used in providing systematic methods to give useful insights from the enormous data that is being generated. Therefore, data science is indeed important.

Other than the general importance, data science is extremely beneficial to the business in numerous ways:

· It provides immense help in decision taking. As discusses, data science analyzes the data in a proper way to solve the problems arising in the business by providing healthy opportunities and unleashing ideas to remove the threats creating issues for the business. It foresees the trends that might surface in the future.

· Data science helps in the business to flourish by improving the product in every which way possible. By tracking the patterns of the consumer’s purchase and knowing about the likes and dislikes, the managers will know what improvisations are required, what kind of product is outdated and what basically is the trend prevailing. Also, by tracing the online trends, the business will be better able to identify the wants of the consumers and produce accordingly to satisfy their needs in an ample manner.

· Similarly, the business will be able to be managed efficiently. Due to the help of data science, the owners will be able to understand the needs of the customers in better way and this will lead to increasing the number of customers. Satisfying the customers efficiently will result in business optimization.

· Data science owns the capability of predicting trends which proves to be a great benefit to the business. To be able to read the patterns and predict the future tendency of peoples’ wants is going to be lucrative in every which way possible.

· Advertisement holds are huge part in the business being able to thrive and the product reaching its target audience. Data science helps in better marketing. Companies need good marketing strategies every day and they analyze their data to create impactful advertisements. Data science can make it easier by making smarter decisions for them and run a campaign for them for the specific purpose.

· Data science holds the future. Industries are becoming data driven and they need data scientists to process and analyze the data for them and make smarter decision by predicting information. Therefore, it holds the career for tomorrow.

· Reading resumes and appointing the right person for the job is a daily task in a company. This exhausting task can be done easily and efficiently through the amount of data available online. Social media and job search websites can be searched thoroughly by the data scientists and select the perfect candidate according to his talents and capabilities.

· Data science can also provide beneficial aid in identifying the right target audience. Data science can prove to be helpful in collecting customer data and gaining insights onto their liking and disliking. The company can learn more about their audience and gain in depth knowledge to target the right group of audience and increase profit margins.


Advantages and disadvantages

Data science is a highly prestigious and versatile career. It also holds great scope in personal growth. It is highly in demand and it holds the future. All the industries realize the importance of the field and all the benefits that it can reap for them and is held as an important position for the company, so it is a highly paid profession. The job is extremely interesting. There are no repetitive tasks to be performed and thus it is not boring at all. Data science is a field that aims in making data meaningful for the company by improving its quality. It makes computers smart enough to read the behaviors and patterns of the customers though their historical purchases and search history. This machine learning phenomenon helps the company in producing better products. However, the field has its disadvantages as well. Data science is a very vague term and it is not easily understandable. Mastering the degree of data science is nearly impossible. To hold proficiency in the field, you require large amount of domain knowledge. Data science helps in predicting future trends but sometimes the results do not yield to be the one as expected. It can happen due to numerous reasons like poor management or scarce resources.


Business intelligence vs Data Science

Data science is commonly mistaken with business intelligence. Business intelligence focuses on analyzing the previous data and run research on it to explain the business trends. It manages, arranges and produces information from the data to answer business problems. It is much simpler than data science. Data science uses complex tools and statistics and analyzes data based on past or current to forecast the future trends. it answers open ended questions as to how and what could happen in the future while BI focuses on the question that asks what happened only. BI has a limited scope as it focuses on past and present, data science focuses on present and future and has unlimited scope. BI contains data that is only structured, while data science contains both structured and unstructured data. BI helps companies in solving their problems while data scientists raise the problems and solve them too. Tools that are used in BI are MS excel, SAS BI, MicroStrategy. Tools used by data science are Hadoop, Qlikview, Python, TensorFlow.


Artificial intelligence vs Data science

Data science makes use of artificial intelligence but they are not entirely the same. Artificial intelligence is known as to counterfeit human intelligence into machines to make them capable of imitating humans and be able to solve problems and make decisions. Data science on the other hand is the process of analyzing, pre-processing and maintaining data for analytics and visualization to forecast future trends and patterns. Data science uses statistical techniques whereas artificial intelligence makes use of algorithms. Data science does not involve scientific processing as much as artificial intelligence. Data science uses data analytics technique while artificial intelligence uses machine learning.


Big data vs Data Science

These two terms are often heard together but they are quite distinguished from one another. Big data is focused on handling large data while Data science focuses on analyzing the data and predicting future outcomes. Big data includes the process of handling large volumes of data and generating insights while data science predicts the outcomes and analyzes the trends and makes rational and smart decisions accordingly. E-commerce, telecommunication and security service industries use big data. While data science plays a huge role in industries involving science, risk analytics, advertisements etc.


Data scientist

Being a data scientist holds great responsibility, proficiency and knowledge in the domain. A data scientist requires to be adept in statistics and mathematics to be able to analyze and process data properly. A data scientist is supposed to be good at machine learning. It is also important that the scientist has great understanding of the domain he is working in to be able to do he is work appropriately. He should be able to apply algorithms where requires and have good knowledge of coding skills. He should also have sufficient experience in working in the field. He should also be good at programming and CS fundamentals. A scientist should also have apt communication skills, as he will be directly in contact with the upper management and will have to communicate his results to them. Its important for a data scientist to understand his projects and fulfill them properly by asking the right questions, using the right resources and tools and then brief the entire process to the stakeholders appropriately to achieve accurate results.

Pay scale of a data scientist

According to glassdoor average salary of a data scientist is $113,309 per year. The pay varies from $83,000 as lowest to $154,000. As for the additional cash compensation, it ranges from $3,850 – $26,084, with an average of $11,258. However, the salary varies according to the industry as well. For example, Facebook pays its data scientist an average of $146,221 per year according to 115 salaries it pays and Microsoft data scientist makes an average of $129,435 per year as per the 79 salaries it gives.



Data science holds its pros and cons and it might take time for it to gain proficiency and momentum but it is without doubt an ever-evolving industry and holds the future. With the humungous outbreak of data, the need for data science will elevate and thus will provide more opportunities to make key businesses decisions. With this evolution, it is also important that data scientists stay motivated to perform their job sincerely and efficiently.