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 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).
Dallas is home to many leading companies, including Finoit, Diceus, Intelegain Technologies, RailsCarma, gotcha!, IBEE Solutions, etc. and all these companies are looking for data scientists to make decisions on product and operating metrics. Because of the exclusivity of skills, the average salary for a Data Scientist is $107,806 per year in Dallas, TX.
Below are the top skills you need to become a data scientist-
Visualization tools such as d3.js, Tableau, ggplot and matplotlib aid a data scientist in the conversion of complex results obtained as a result of the processes performed on a data set and converts them into a comprehensible visual format.
With diverse datasets coming in, working with unstructured data aids to the 360-degree approach expected of a data scientist. Unstructured data is content that is not labelled and organized into database values such as videos, social media posts, audio samples, customer reviews, blog posts etc.
A successful Data Science professional has the following behavioral traits:
There are many benefits of ‘Sexiest job of the 21st century’ by Harvard Business review:
To become a successful data scientist, you need to have the following business skills:
Below are the best ways to brush up your data science skills for data scientist jobs:
Boot camps are the perfect way to brush up your Python basics. They usually last 4 to 5 days. These boot camps offer theoretical knowledge and hands-on experience.
Certifications provide you an additional skill set and help improve your CV. Some of the famous data science certifications are:
These are online courses that include the latest trends in the industry. These are taught by data science experts and help polish implementation skills in the form of assignments.
Projects help you to explore new solutions to already answered questions depending upon the project constraints. The more you work on projects, your thinking and skills will get more refined.
Competitions like Kaggle, etc. helps you to improve your problem-solving skills by giving restraints and forcing you to find an optimum solution.
We live in a world of data. Most companies in Dallas, Texas including Finoit, Diceus, Intelegain Technologies, RailsCarma, gotcha!, IBEE Solutions, etc., collect data for their own benefit and these data tends to improve their customer experience. You may also get to work with mid-size and small size companies. Mid-size companies have data but they need someone to apply ML techniques to leverage it. Small companies use Google Analytics for their analysis because they have fewer resources and fewer data to work.
Some ways to practice your data science skills are given below:
The Iris data set is said to be the easiest data set. This is the best data set for a beginner and consists of merely 4 columns and 50 rows.
Practice Problem: Predict the class of a flower on the basis of their parameters.
The Loan Prediction data set provides the learner the concepts that are applicable in the domain of banking and insurance - the challenges faced, the variables that influence the outcomes, etc. It consists of 13 columns and 615 rows and is a classification problem data set.
Practice Problem: Predict if a given loan will be approved by a bank based in Dallas or not.
Operations such as Product Bundling, offer customizations, inventory management, etc. are efficiently handled with the help of Data Science and Business Analytics. The Big Mart Sales Data Set is used in Regression problems and consists of 12 variables and 8523 rows.
Practice Problem: Predict the sales of a retail store of Dallas, Texas.
The Black Friday Data Set has sales transactions from a retail store. It is an apt data set to expand and explore engineering skills .It has 12 columns and 550,069 rows and is a regression problem.
Practice Problem: Predict the amount of total purchase made on a day in Dallas, Texas.
The Human Activity Data Set has a collection of 30 human subjects that were collected via recordings by smartphones. It consists of 561 columns and 10,299 rows.
Practice Problem: Predict the human activity category.
This data set consists of aviation safety reports that describe the problems that were encountered on a certain flight. The Text Mining Data Set consists of 30,438 and 21,519 columns. It is a high dimensional and multi-classification problem.
Practice Problem: Classify the documents on the basis of their labels.
The Urban Sound Classification data set is for implementation of Machine Learning concepts to real-world problems by audio-processing. It consists of 8,732 sound clippings of urban sounds that can be categorized in 10 classes.
Practice Problem: Classify the type of sound that is obtained from particular audio.
This data set comprises of 7000 images, totaling 31MB, with dimensions of 28X28 each. It allows the developer to study, analyze and recognize the elements present in an image.
Practice Problem: Identify the digits present in a given image.
The Vox Celebrity Data Set is for large scale speaker identification and speech recognition. It is a collection of words spoken by celebrities and extracted from YouTube videos. This data set consists of 100,000 words spoken by 1,251 celebrities around the world.
Practice Problem: Identify the celebrity that a given voice belongs to.
Below are the steps to become a successful Data Scientist:
Underneath, we have compiled some of the key skills & steps required to get started.
Almost 88% of data scientists in Dallas, Texas hold a Master’s degree while 46% are Ph.D. degree holders. Getting a degree is not mandatory but it may help you in networking, internship and recognized academic qualifications in your résumé.
If you are struggling in deciding whether a Master’s degree in data science is right for you or not, here is a scorecard that will help you decide the same. If your total score is more than 6 points, you must get a Master’s degree:
Knowledge of programming is the fundamental skill for a data scientist.
Underneath, some reasons are listed:
Here is a logical sequence of steps which you should follow to get a job as a Data Scientist in Dallas, Texas:
Here are the 5 steps you must take if you are preparing to get a job as a data scientist:
Some of the major roles & responsibilities of a Data Scientist are:
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:
A career path in the field of Data Science can be explained in the following ways:
There are several career options for a data scientist –
Mastery over the following tools or software will help you get preferred over other data scientists:
Python is a programming language which is multi-paradigm. Python works on a very simple interface and has high readability. The language also has a wide range of resources which makes it a choice language among the Data Scientists.
Knowledge of programming languages is a must for a job in the field of Data Science. Here are the 5 most popular programming languages commonly used for Data Science:
To download and install Python 3 on Windows, you should follow these steps:
Python -m pip install -U pip
It is very easy to download and install Python 3 on Mac OS X. You can use the .dmg package as well as Homebrew as it makes it easier to install the dependencies as well. You may follow these steps to install Python 3 on Mac OS X.
$ xcode-select --install
To confirm if it is installed, you may type brew doctor.
brew install python
To confirm its version, you should use: python --version
You should also install virtualenv, which shall help you create isolated places to run different projects and may run on different versions of python.
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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.
Any registration canceled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% deduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of a written request for refund. Kindly go through our Refund Policy for more details.
In an online classroom, students can log in at the scheduled time to a live learning environment which is led by an instructor. You can interact, communicate, view and discuss presentations, and engage with learning resources while working in groups, all in an online setting. Our instructors use an extensive set of collaboration tools and techniques which improves your online training experience.
Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor