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 analysing, 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 an 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.
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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).
Harvard Business Review defined Data Scientist as the “sexiest” job of 20th century in 2012. San Jose is home to many leading companies, such as PayPal, Adobe, eBay, Cisco, Broadcom, Verifone, etc. These companies are looking for expert data scientists to make smart business decisions.
There are other reasons as well that make Data Scientists hugely popular in today’s world.
The basic skills required to become a Data Scientist are more or less similar in countries around the world. The skills also vary according to the requirements of the company and the experience requirement. Knowledge and experience in the following are, however, essential:
Most companies define their own job profiles and characteristics that they are looking for in a Data Scientist. However, there are some basic traits that a Data Scientist should possess:
The Harvard Business Review considered it as the “sexiest job of the 21st century”. Understandably, there are some major benefits that you can earn as a Data Scientist. Some of them have been explained below:
High Salary: Companies at present are willing to shell out higher salaries for experts in data science. This is because of the demands in the market for Data Scientists and the nature of the job, itself. Data Scientists need to be proficient in at least two programming languages, understand data and financial analysis, consider the consequences of their decisions based on data analysis and then support companies’ marketing decisions. As the demands from this profile are high, so are the salaries.
Bonus: Apart from the high salaries that companies pay to Data Scientists, there are also bonuses that the employee can get. Major companies also offer equity shares to Data Scientists.
Academics: In order to become a Data Scientist, one must be strong academically. Companies usually hire those who have a Masters or a PhD in the field. Hence, when you finally bag a lucrative job, be it as a professional in a major company or a researcher in universities, you have acquired a fair amount of knowledge in the field.
Lifestyle: Data Scientist jobs are mostly offered in places that are well-connected and developed. So, apart from the high salary that you will be drawing, your standard of living will also improve to a great extent.
Networking: As a Data Scientist, you will be able to come in contact with a lot of experts. You might also get invited to tech talks, which is likely to expand your social as well as professional network.
As a Data Scientist, you need to have the following skills
As a Data Scientist, you can improve your Data Science skills by being involved with any or all of the following ways-
Boot Camps: Boot Camps are an excellent way to help you practice your Python skills. These camps are usually 4-5 days long and are effective if you want to improve both your practical as well as theoretical knowledge.
Massive Open Online Courses: These Online Courses are delivered by experts in Data Science. You will also get the chance to stay updated about the latest trends and practice your skills on assignments.
Getting certified: Getting certified will help you with improving your skill sets. Also, certifications will add credibility to your CV when you start applying for jobs. The following are some of the most important Data Science certifications that you can get:
Taking part in competitions: Taking part in competitions will enhance your capabilities to work with limited constraints and also work effectively to find out the solutions to the problems.
Data is everywhere in today’s world. Every form of information that you need or will possibly need starting from your investment details and your medical bills to your phone number and residence address is data. Companies use this data for their marketing purposes and enhance the customer experience that they offer you. Verifone, Fair Isaac Corporation, Cisco, PayPal, Adobe, eBay, Broadcom, etc. are some of the companies that are hiring in San Jose.
The best way to master anything is certainly to practice it over time. This applies to Data Science as well. In order to solve Data Science problems, you will certainly have to work on various aspects of Data Sets and understand what might work for you. Here, we have categorized different problems according to their difficulty level and your expertise level:
Below are the steps that you must follow in order to become a top-notch Data Scientist:
Starting out: The first step involves choosing the right programming language. Python or R are most commonly used languages.
Learning Mathematics and Statistics: These form the basis of your knowledge. As a Data Scientist, you will have to go over various forms of data including numbers, texts and images, and analyze them by seeking out patterns. You need to have a basic understanding of statistics and algebra.
Visualizing Data: Data Scientists also need to work in teams, and for you to be a good Data Scientist, communication will be crucial. Visualizing Data involves analyzing data and communicating the information to your non-technical peers in a way that they understand.
Machine Learning and Deep Learning: For every data scientist, it is a must to have basic Machine Learning skills along with deep learning skills in their CV. With these, you will be able to analyze any data given to you.
In order to ease your path to become a Data Scientist, we have listed some of the steps and key skills required to help you kickstart your career as a data scientist:
At least 46% of Data Scientists hold a PhD degree, so, it is important that you continue with higher studies after Graduation to land a lucrative job. Below are some other benefits of getting a degree:
Helps with networking: Throughout your entire journey as a student and learner, you will get to meet and work with people who share the same kind of interests. This is going to help you immensely in the future when you will start working as a Data Scientist.
Being organized about learning: When you are pursuing a degree, you will have to keep up with the curriculum and follow a particular schedule. This is more beneficial and effective than studying without any planning.
Opportunities for Internships: Degrees will help you find the appropriate internship opportunity, which in turn, will help you get a job. You will also gain practical knowledge through the same.
Earning credibility: Lastly, earning a degree is not only about the knowledge that you gain as a student, but also is about the credibility that it adds to your CV. You will have something to show and speak for your expertise and skill, if you have a degree from a reputed institute.
If you are having trouble in deciding whether you should go for a Master’s degree, you can try grading yourself on the basis of the below scorecard. If your score is more than 6 points, you should get a Master’s degree:
Having programming knowledge is one of the most critical aspects of being a Data Scientist. The main reasons are as follows:
Helps with working on Data Sets: As a Data Scientist, you will be required to work with huge amount of data. Programming knowledge will help you with such large data sets analysis.
Helps with Statistical application: A data scientist has to work with statistics. You need the ability to program to implement statistics. Without the knowledge of programming language, knowledge of statistics does not do much good.
Helping out with Framework: Programming knowledge is also useful when trying to apply data science in the most effective way possible. Developing Frameworks becomes easier which, in turn, can be used to make sure that the right data is accessible.
If you want to get a job in the field of Data Science, you need to follow this path:
Starting out: Learn a programming language that you will be comfortable with. The most commonly used programming languages in Data Science are Python and R language.
Learning Mathematics: This is critical, since, as a Data Scientist, you will be working with raw data. Having a strong hold on Mathematics and Statistics will be helpful. You need to pay special attention to Descriptive statistics, Probability, Inferential Statistics to further your knowledge.
Understanding Libraries: This is important to perform tasks like data processing and for structured data plotting. Some of the most common libraries are SciPy, ggplot, Matplotlib and others.
Understanding Data visualization: Another important aspect of a Data Scientist job is to find patterns in unstructured data and to communicate the same to people who are from non-technical backgrounds. Therefore, data visualization becomes important. The libraries used for this task are ggplot2 and matplotlib.
Understanding data pre-processing: The unstructured nature of data makes it important for data scientists to pre-process the data before making it ready for analysis. This is usually done through feature engineering and variable selection.
Machine Learning and Deep Learning: Deep learning algorithms are used while dealing with a huge set of data. You need to have a tight grasp on topics like CNN, RNN, Neural networks, etc.
Learning Natural Language processing: This is important to understand how the text form of data can be processed and classified.
When preparing for a Data Scientist Job, you will need to go through the following steps to be able to increase your chances.
The main role of a Data Scientist is to make sense of the huge amount of data that is being generated on a daily basis and make it business ready. First, you need to get the data that is relevant to the business from the huge amount of data provided to you. This data can be in structured as well as unstructured form. Next, this data needs to be organised and analysed. After this, you need to create machine learning techniques, tools and programs to identify patterns in the data and make sense out of it. Lastly, you need to perform statistical analysis on the data to predict future outcomes.
Salaries of Data Scientists depend on the type of company and the job profile. Depending on the roles and responsibilities of a Data scientist, the average pay scale is as follows:
A Data Scientist will work with huge volumes of data and predict the outcome based on the same. The whole career path of a Data Scientist can be explained as follows:
The most effective way is to go through referrals. Other ways to hire Data Scientists for the team include Data Science Conferences, LinkedIn and Social Meetups.
As of 2019, a Data Scientist can look at the following career opportunities:
Employers generally prefer data scientists to have mastery over some software and tools. They generally look for:
Python is one of the most commonly preferred languages used by Data Scientists because of its simplicity and readability. It is an object-oriented, structured programming language that comes with several packages and libraries that can be beneficial in the field of Data Science. The other benefit of using Python as a programming language in Data science is the vast community dedicated to the language.
Following are some of the most popular programming languages commonly used for Data Science:
Here is how you can download and install Python 3 on Windows:
python -m pip install -U pip
For installing Python 3 on Mac OS X, you can either simply install the language from their official website using a .dg package or use Homebrew python or its dependencies. Here are the steps you need to follow:
/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
Confirm if it is installed by typing: brew doctor
brew install python
If you want to confirm the version of python, use the command: python --version
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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