Rapid technological advances in Data Science have been reshaping global businesses and putting performances on overdrive. As yet, companies are able to capture only a fraction of the potential locked in data, and data scientists who are able to reimagine business models by working with Python are in great demand.
Python is one of the most popular programming languages for high level data processing, due to its simple syntax, easy readability, and easy comprehension. Python’s learning curve is low, and due to its many data structures, classes, nested functions and iterators, besides the extensive libraries, this language is the first choice of data scientists for analyzing, extracting information and making informed business decisions through big data.
This Data Science for Python programming course is an umbrella course covering major Data Science concepts like exploratory data analysis, statistics fundamentals, hypothesis testing, regression classification modeling techniques and machine learning algorithms.
Extensive hands-on labs and interview prep will help you land lucrative jobs.
Get acquainted with various analysis and visualization tools such as Matplotlib and Seaborn
Understand the behavior of data;build significant models using concepts of Statistics Fundamentals
Learn the various Python libraries to manipulate data, like Numpy, Pandas, Scikit-Learn, Statsmodel
Use Python libraries and work on data manipulation, data preparation and data explorations
Use of Python graphics libraries like Matplotlib, Seaborn etc.
ANOVA, Linear Regression using OLS, Logistic Regression using MLE, KNN, Decision Trees
There are no prerequisites to attend this course, but elementary programming knowledge will come in handy.
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Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.
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Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.
Get reviews and feedback on your final projects from professional developers.
Get an idea of what data science really is.Get acquainted with various analysis and visualization tools used in data science.
Hands-on: No hands-on
In this module you will learn how to install Python distribution - Anaconda, basic data types, strings & regular expressions, data structures and loops and control statements that are used in Python. You will write user-defined functions in Python and learn about Lambda function and the object oriented way of writing classes & objects. Also learn how to import datasets into Python, how to write output into files from Python, manipulate & analyze data using Pandas library and generate insights from your data. You will learn to use various magnificent libraries in Python like Matplotlib, Seaborn & ggplot for data visualization and also have a hands-on session on a real-life case study.
Visit basics like mean (expected value), median and mode. Understand distribution of data in terms of variance, standard deviation and interquartile range and the basic summaries about data and measures. Learn about simple graphics analysis, the basics of probability with daily life examples along with marginal probability and its importance with respective to data science. Also learn Baye's theorem and conditional probability and the alternate and null hypothesis, Type1 error, Type2 error, power of the test, p-value.
Write python code to formulate Hypothesis and perform Hypothesis Testing on a real production plant scenario
In this module you will learn analysis of Variance and its practical use, Linear Regression with Ordinary Least Square Estimate to predict a continuous variable along with model building, evaluating model parameters, and measuring performance metrics on Test and Validation set. Further it covers enhancing model performance by means of various steps like feature engineering & regularization.
You will be introduced to a real Life Case Study with Linear Regression. You will learn the Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis. It also covers techniques to find the optimum number of components/factors using screen plot, one-eigenvalue criterion and a real-Life case study with PCA & FA.
Learn Binomial Logistic Regression for Binomial Classification Problems. Covers evaluation of model parameters, model performance using various metrics like sensitivity, specificity, precision, recall, ROC Cuve, AUC, KS-Statistics, Kappa Value. Understand Binomial Logistic Regression with a real life case Study.
Learn about KNN Algorithm for Classification Problem and techniques that are used to find the optimum value for K. Understand KNN through a real life case study. Understand Decision Trees - for both regression & classification problem. Understand Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID. Use a real Life Case Study to understand Decision Tree.
Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data. Work on a real- life Case Study with ARIMA.
A mentor guided, real-life group project. You will go about it the same way you would execute a data science project in any business problem.
Project to be selected by candidates.
With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.
This project involves building a classification model.
Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.
Wine comes in various styles. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).
In 2012, Harvard Business Review dubbed Data Scientist the sexiest job of the 21st Century. Companies like Google, Facebook and others collect user data and sell them to ad companies to earn profits. How do you think they know whether you like dogs or cats? How do you think Amazon knows what products to recommend to you even when they haven’t explicitly asked you about it? The answer is data. Some other major reasons why data science is popular are:
Therefore, Data Science is in demand both from a company’s and from an employee’s perspective.
The top skills that are needed to become a data scientist include the following:
Below are the top 4 behavioral traits of a successful Data Scientist -
There are many benefits to being in the job declared as the ‘Sexiest job of the 21st century’ by Harvard Business review:
If you’re considering a career in data science, there are 3 educational paths that can help you get started.
A report published in May 2017 suggested that in a field like data science, academic qualifications are highly valuable. Reportedly 90% of interviewed data scientists reported to obtaining an advanced degree – 49% held a master's and 41% held a PhD.
Data science is a vast world with a large open source community with many sub-fields. As a beginner, you're bound to make a few mistakes. However, one of the most common mistakes that amateur data scientists make includes choosing a library that is not the most appropriate one for the task at hand.
Many times rather than taking into consideration the type of data we have, constraints, and the aim of our project, we simply choose a library because it is the most popular one or one with a plethora of features. It is important to know that the most popular libraries are not always the ones which are best suited for our problem.
Some of the other common mistakes include-
Data Science and Machine Learning go hand in hand. While Machine Learning is the ability of a machine to find patterns from data, Data Science is the mechanism by which the machines are provided with data. The more the availability of data, the more is the complexity and difficulty in compiling new predictive models that are able to accurately and efficiently work on this data. This is where the role of Machine Learning comes in, to help Data Scientists make sense of the large amounts of data they have and to convert it into meaningful information.
As a data scientist, you have to deal with all kinds of data-numbers, text, image, etc. Natural Language Processing (NLP) helps us deal with the textual form of data and use it in our computations and algorithms. Some of the important applications of NLP are:
Below are the technical skills that you need if you want to become a data scientist.
Want to know more about the data scientist skills?
We have listed down all the essential Data Science Skills required for Data Science enthusiasts to start their career in Data Science
Below is the list of top business skills needed to become a data scientist:
Data science may not be as much about communication as it is about data, but however good a data scientist is, he must remember that data science is not all about crunching numbers. One of the main responsibilities of a data scientist is to communicate customer analytics as well as business insights to his customers.Data scientists must also remember that no technology exists in a vacuum in the business environment of today. There always exists some level of integration between data, its applications as well as the people.Thus, being able to communicate with stakeholders is a skill that every data scientist must have. Communicating with and understanding the requirements of a customer is another key priority that requires a data scientist to have good communication skills.
From its use in enabling the faster analysis of information as well as the ease it offers in recognizing trends and patterns in a given set of data, data visualization is proving to be increasingly useful in the field of Data Science. No matter the size of the organization, every company with an eye towards the future is harnessing the power of data visualization. For the same reason, every company in the world, no matter how big or small, is looking for data visualization experts who can channel this power of data visualization and use it for the faster progress of the company. While other skills are also important in a data scientist, studies and surveys increasingly show that the ability of a data scientist to use and visualize data is a highly sought after skill in the job market these days, which is also a trend that is unlikely to stop in the foreseeable future.
The role of the data scientist is, no doubt, one of the hottest jobs in the market today and becoming a data scientist demands an ardent passion for knowledge. We have compiled a list of key points to help you decide whether data science is right for you or not.
Below are the best ways to brush up your data science skills for data scientist jobs:
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:
The best way to master the art of Data Science is to practice and work your way through the challenges you face while solving Data Science problems. Some ways to practice your data science skills are to work on the following data science problems, categorized according to their difficulty level as compared to your expertise level:
Apache Spark is a general, multipurpose engine that is used for the processing of large scale data. It is an open source, in-memory distributed computing engine that was developed in the AMPLab at UC Berkeley. It is a computing engine which is highly versatile in any given environment. Apache Spark is basically an advanced analytical tool that is useful for the implementation of Machine Learning algorithms.Apache Spark is also 100 times faster as compared to Hadoop MapReduce in the system memory and 10 times faster on the disk. Apache Spark is seen by many experts as the answer to the problems and inefficiencies produced by the use of MapReduce. Some other reasons for the popularity of Apache Spark include the following:
Below are the right steps to becoming a data scientist:
The job of a Data Scientist has been declared as “The Sexiest Job of the 21st Century” by none other than Harvard Business Review. So how do you prepare for a career in data science? Don’t worry, we have compiled some of the key skills & steps required to get started.
Data scientists are some of the most educated professionals in the IT field. Almost 88% of data scientists hold a Master’s degree while 46% of all data scientists are PhD degree holders. While there exist notable expectations for this trend, a strong educational background is one of the most observed backgrounds in data scientists.
In order to become a Data Scientist, you may take a Bachelor’s degree in Social Sciences, Statistics, Computer Science or Physical Sciences. The most common backgrounds that Data Scientists possess in the order of their popularity include Mathematics and Statistics (32%), Computer Science (19%) and Engineering (16%). After obtaining a Bachelor’s degree, most Data Scientists have either pursued a Master’s degree or PhD as well as have undertaken online training in a related field.
As mentioned before, almost 88% of data scientists hold a Master’s degree while 46% of all data scientists are PhD degree holders.
A degree is very important because of the following –
The best way to determine whether you need a Masters in Data Science is by grading yourself on the scorecard below. If your total adds up to more than 6 points, it would be advisable for you to earn a Master’s degree.
Knowledge of programming is perhaps the most vital and fundamental skill that an aspiring data scientist must possess. Some of the other reasons why knowledge in programming is required include:
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:
Below are some of the topics that are must in statistics:
Yes, knowledge of Structured Query Language (SQL) is required in order to become a data scientist. Data Scientists need to be able to retrieve data, in order to actually process it, analyse and make use of it. The main use of SQL for data scientists is for the retrieval of data, although some uses of data modelling and creation of a test environment may also crop up from time to time.
The job of a data scientist is not to administer or build a Hadoop cluster, but to glean useful insights from the data that is available, no matter where it comes from. Each data scientist must be able to obtain data in order to perform an analysis and Hadoop is the technology that enables the storage of large volumes of data for a data scientist to work on. So no, you do not NEED to learn Hadoop in order to become a Data Scientist, but you do need to learn some or the other tool that is similar to Hadoop.
Computer vision is used for crowd analytics, emotion analysis, verification, identification, and recognition of the image. Companies like Facebook, Instagram etc. collect image data (along with other data) from users on a daily basis. Some of the popular computer vision applications are:
Most data scientists have a PhD or master's degree, which clearly indicates how competitive this field is. Having a certification in data science can have a great impact on your overall profile. We have compiled a list of some of the best and popular certifications for you:
We at Knowledgehut, conduct Data Science course with Python training in all the cities across the globe and here are a few listed for your reference:
|Ottawa||Kuala Lumpur||Chicago||San Diego|
|Chennai||Cape Town||Fremont||San Jose|
We have compiled our learning path in logical sequence to help you delve into it successfully.
Below are the top short courses in data science-
Data science is a huge field and covering everything about data science is not possible. So it is highly advised to decide what is your area of interest in this field. There are two ways to decide what kind of data science course you want to pursue:
A data scientist is an individual who is responsible for discovering patterns and inferencing information from vast amounts of structured as well as unstructured data, in order to meet the business goals and needs.In this modern business scenario that is generating tons of data every day, the role of a Data Scientist is becoming all the more important. This is because the data generated is a gold mine of patterns and ideas that could prove to be very helpful in the advancement of a business. It is up to the data scientist to extract the relevant information and make sense of it in order to benefit the business.
Data Scientist Roles & Responsibilities:
Data scientist has been declared as the hottest job of the 21st century. Due to high demand and less number of data scientists, data scientists earn base salaries up to 36% higher than other predictive analytics professionals. The salary of a data scientist depends on 2 things:
There are several career options for a data scientist –
A Data Scientist is an individual who has the combined abilities of a mathematician, a computer scientist, and a trend spotter. The job of a Data Scientist is to decipher large volumes of data, mine the relevant parts of this data and then analyze this data so as to make predictions for similar data in the future. A career path in the field of Data Science can be explained in the following ways.
Business Intelligence Analyst: A Business Intelligence Analyst is an individual who has the job of figuring out the business as well as the market trends. This he/she does by the analysis of data in order to develop a clear picture of where exactly the business stands in the business environment.
Data Mining Engineer: A Data Mining Engineer is an individual who has the job of not only examining the data for the needs of the business, but also for the benefit of a third party. In addition to his job of the examination of data, a Data Mining Engineer also needs to create sophisticated algorithms that further aid in the analysis of data.
Data Architect: The role of a Data Architect is to work in tandem with system designers, developers and users in order to create blueprints that are used by data management systems in order to integrate, protect, maintain as well as centralize data sources.
Data Scientist: The main responsibility of a Data Scientist is to pursue a business case by analysis, development of hypotheses as well as the development of an understanding of data, so as to explore patterns from the given data. Data Scientists then move on to the development of algorithms and systems that make use of this data in a productive manner so as to further the interests of business.
Senior Data Scientist: A Senior Data Scientist is tasked with anticipating future business needs and shaping the projects, systems and data analyses of today to suit those business needs in the future.
If you are thinking to apply for a data science job, then follow the below steps to increase your chances of success:
Below are the top professional organizations for data scientists –
Referrals are the most effective way to get hired. Some of the other ways to network with data scientists are:
Due to high demand and low supply in case of data scientists in the industry, the expectations from them are also high. However, this means that the recognition and career benefits (like salary) are exceptionally high as well. If you are aspiring to be a data scientist then we have compiled key points, which the employers generally look for in data scientists while hiring:
We have compiled the key points, which the employers generally look for while hiring data scientists:
There are many factors that make a program a success. Like every other educational field, the advancement in Data Science also depends on multiple reasons.
Data Science deals with identification, representation, and extraction of meaningful information, so any programming language equipped with tools to do these tasks efficiently will be naturally popular. Python is one such popular language and the reasons for the same include:
As data science is a huge field and involves multiple libraries to work together in a smooth way, it is essential that you choose an appropriate programming language.
Follow these steps to successfully install Python 3 on windows:
Alternatively, you can also install Python via Anaconda as well. Check if Python is installed by running the following command, you will be shown the version installed:
python -m pip install -U pip
Note: You can install virtualenv to create isolated Python environments and pipenv, which is a Python dependency manager.
You can simply install Python 3 from their official website through a .dmg package, but we recommend using Homebrew to install Python as well as its dependencies. To install Python 3 on Mac OS X, just follow the below steps:
brew install python
You should also install virtualenv, which will help you create isolated places to run different projects and may run even on different python versions.
Follow the below steps to successfully install Python 2 on your windows:
C:\Python2x This helps in installing multiple versions of Python on your windows machine.
Unstructured data refers to the undefined contents of a data set that cannot be fit into structured database tables. It is basically information that is not organized in a predefined manner nor has a data model that is pre-defined. Unstructured data is generally text-heavy but may also consist of other data such as numbers, facts, figures, audio, video etc.
While unstructured data may be difficult to organize, if a company is able to tap into it in a meaningful and efficient manner, it is like digging up a bag of gold.Unstructured data can aid companies in the formation of important business decisions if a company is able to integrate this unstructured data into their information management systems and landscapes.
Pandas and NumPy are two of the most used Python libraries for data manipulation. Most of the times they are used in a single project. Although Pandas is a library build directly off from NumPy, there are some differences between both of them.
Tabular form - CSV or SQL formats
Helps add, edit, or create columns or rows to the table.
Helps perform multiple operations on Array.
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.
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Python is a rapidly growing high-level programming language which enables clear programs on small and large scales. Its advantage over other programming languages such as R is in its smooth learning curve, easy readability and easy to understand syntax. With the right training Python can be mastered quick enough and in this age where there is a need to extract relevant information from tons of Big Data, learning to use Python for data extraction is a great career choice.
Our course will introduce you to all the fundamentals of Python and on course completion you will know how to use it competently for data research and analysis. Payscale.com puts the median salary for a data scientist with Python skills at close to $100,000; a figure that is sure to grow in leaps and bounds in the next few years as demand for Python experts continues to rise.
By the end of this course, you would have gained knowledge on the use of data science techniques and the Python language to build applications on data statistics. This will help you land jobs as a data analyst.
Tools and Technologies used for this course are
There are no restrictions but participants would benefit if they have basic programming knowledge and familiarity with statistics.
Yes, KnowledgeHut offers virtual training.
On successful completion of the course you will receive a course completion certificate issued by KnowledgeHut.
Your instructors are Python and data science experts who have years of industry experience.
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Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor