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.
Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.
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).
Data science has been named the sexiest job of the century and with good reason too! In the Baltimore city of Maryland, several companies like Amazon, ShoreIT Solutions, CGI Group, Inc, Booz Allen Hamilton, Bethesda Softworks, etc. are looking for data scientists to help them in optimizing their business. This emerging technology deals with computing, collecting, storing and analyzing data to derive meaningful insights that can be beneficial to enterprises. Ever wondered how big shot companies like Facebook or Google show ads that cater to your personal preferences? The answer is, through the data gained from your routine online activities. Data science allows engineers and entrepreneurs to gauge market trends and accurately predict any future changes effectively. Some other reasons that make data science a popular career choice are:
There are several institutions in Baltimore, MD that offer degrees in Data Science including the Johns Hopkins University, Loyola University, Notre Dame of Maryland University, and University of Maryland. As a data scientist one must be aware of multiple programming languages, be a pro at coding, and master platforms for sorting and analyzing data. The top skills that are needed to become a data scientist include the following:
Simply having the necessary technical skills is not enough to become a data scientist. A good data scientist must also possess certain behavioral traits that would make him an asset to the organization. Below are the top 5 behavioral traits of a successful Data Scientist –
There are several companies in Baltimore, MD offering data science jobs like HyreU, Louis Berger, AIC, Williams Consulting LLC, WGSN, IT America Inc, Iterum, etc. Data science as a career opportunity comes with a lot of scope to explore. Here are some advantages of being a data scientist that might motivate you to opt for the profession.
As a data scientist, your job is not strictly technical. You would be expected to fill in the role of an engineer, an analyst, a manager, a mathematician, and even a marketer. You will have to hold meetings, collaborate with other departments, convince investors and build data sets that would eventually influence market demand. Below is the list of top business skills needed to become a data scientist:
Data science is a very challenging and demanding job which requires constant effort and practice. One has to keep up with the latest trends in the industry, the dynamics of the market and always be ready to absorb and analyze information. Below are the best ways to brush up your data science skills for top data scientist jobs in Baltimore, MD:
Data is crucial to the world we live in, in fact, everything you see around is manipulated by information and technology. Whatever you see in the virtual world is a manifestation of all the information that data scientists have gathered in their databases. Now, data science is important for pretty much every industry, irrespective of the sector it falls in. There are several corporations located in Baltimore, MD that employ Data Scientists including Amazon, ShoreIT Solutions, CGI Group, Inc, Booz Allen Hamilton, Bethesda Softworks, HyreU, Louis Berger, AIC, Williams Consulting LLC, WGSN, IT America Inc, Iterum, etc.
Here are some popular data sets that you can practice on, depending on your skill level;
Here are the steps that you must follow in order to become a top-notch Data Scientist:
Here’s what one needs to do in order to kick start their career in data science:
Here is how your degree helps in securing a good position in data science:
A postgraduate degree in data science is not mandatory, however, most employers consider only applications where the candidates have a credible academic degree to back their experience in data science. There are several institutions in Baltimore where you can apply for a masters course. If you are considering getting a higher degree in data science, consider your intelligence and caliber for the subject as well.
Data science requires not just a theoretical understanding of stats and maths but also an in depth understanding of coding and how programming platforms like Python works. The platform sets the fundamental framework for the data sets to be stored and analysed in. Here are some ways in which programming aids your career data science;
If you're a budding data scientist looking for a way to get into the IT sector and build a career in data science here is what you have to do:
Here is how one can prepare for a career in data science
A data scientist is responsible for figuring out patterns and extracting information from structured and unstructured data. In this current business context, the role of a Data Scientist has become even more crucial. The data generated and arranged by the data scientist is used to monitor and manipulate patterns, changing market trends and more.
Data Scientist Roles & Responsibilities:
https://www.umbc.edu/The average salary for a Data Scientist is $110,316 per year in Baltimore, MD, which is 11% below the national average. Data science has a high scope for growth and exposure in baltimore. Data scientists are not just needed in the IT sector but also in other industries like banking, medical, hospitality and corporate sector alike.
Data science has a vast scope for research and career growth, as in this modern day and context, data is the be all and end all of all business decisions taken up by companies. A data scientist is supposed to fill the role of a software engineer, a marketer, a trendsetter and a mathematician. He has to work with huge volumes of data set, often unstructured information, curate what’s relevant out of it and then gather insights that would help the organization predict customer trends and preferences with as much accuracy as possible. Here are some of the things you can be as a data scientist:
Data Analyst: As a data analyst you will be required to study the market trends, observe customer preferences and have a solid idea about the demographics that your company is targeting. This helps create a clear plan on the kind of strategies and advertising policies you would want to set up.
Data Scientist: Data scientists have a tougher job than just detecting and recording marketing trends. As a data scientist, your job will entail tasks like evaluating gigantic volumes of data, noticing patterns, developing a premise and creating processes based on the same. Data experts also have to deal with programming and hence you must have sharp coding skills.
Data Engineer: As a data engineer your job involves gathering data sets, inspecting the business requirements involved, cooperating with third-parties, creating algorithms and curating data sets that would eventually help forecast market trends with as much exactness as possible. As a data engineer, you would also have to design data related operations, think of ground-breaking solutions and study the given information rationally.
Data Architect: A data architect has to often work together with data scientists and engineers to create intricate plans for the corporate body. The data architect is accountable for the workings of the plan. He has access to all the core codes and the data source which he assimilates and adds on to the data for improved results.
Baltimore, Maryland is one of the most up-to-date destinations for data scientists. It offers undergraduates and reputable professional developers a great atmosphere for research. Plus, there is no shortage of establishments that are eager to hire the best data scientists and engineers offering them amazing pay packages and other perks. If you are a data scientist looking for a reputed organization to work, there are a few places you should definitely check out:
After you have completed your data science course and have equipped yourself with the necessary skills to make a mark in the industry the next step is to make yourself visible to the big shots of the industry. Oftentimes your college or institution would organize job fairs and campus selection programs where you can connect with the top companies and corporate houses and showcase your work. Another way to get hired is via referrals. Here are some areas where you can expand your contacts and network with other data scientists as well:
Data scientists have to be aware of how to handle information, how data is collected, arranged and distributed across platforms. You’ll be required to do a lot of things- like being a coding expert, manager, technician, marketer and more. However, there are some core skills that every company wants. Let’s find out what these skills are:
General Skills: General skills are the core theoretical and academic credentials required of a data scientist. Most data scientists have a Ph.D., a degree in Machine Learning and AI and a few research papers to their name
Technical Skills: Technical skills involve an in-depth knowledge of programming languages like Python, R Programming, SQL, Hadoop, Spark, JAVA, SAS, Hive, Jsp.net, C++, NSQL, AWL, Scala and more.
Practical Skills: What one learns from a textbook is very different from what one sees in real life. Often, a data scientist will have to solve problems that are not taught in the classroom or in the coursework. Practical experience hence becomes an important aspect of your training.
Read on to know why Python is best for beginners;
techniques and platforms commonly used; which include:
R Programming: R is an open source software that allows users to compute huge data sets, get statistical insights, create custom graphics and more. The platform has a steep learning curve but is extremely quick and effective once you understand it. It includes;
Python: Python is an easily accessible data tool great for analyzing, arranging and integrating data into complicated data sets and creating advanced algorithms. It is among the most sought after platform by most data scientists. this is because:
SQL: SQL or structured query language comes easy for every data scientist and for good reason. The platform allows users to arrange data, edit the unstructured, creating relational databases and more. One can even store databases, retrieve old data sets, and gain quick and immediate insights. Other perks include:
Java: JAVA runs on the JVM or Java Virtual Machine Platform and is used by almost all MNCs and Corporations to design backend systems and applications. Some advantages of using Java are;
Scala: Scala is based on JVM and hence is an ideal choice for data scientists to run massive data sets. The coding interface, intuitive tools, and a powerful static tape framework makes the platform even more reliable;
Here are a few simple and effective steps using which one can download and install Python 3 on your Windows platform;
Downloading Python 3: First, check whether your desktop is compatible with the new version of Python 3. Windows do not usually come with a Python program pre-installed. Visit the download page for Python, python.org and click on the link for the Latest Python 3 Release - Python 3.6.5. You then have to scroll to the GUI installer and select from either Windows x86-64 executable installerfor 64-bit or Windows x86 executable installerfor 32-bit.
One can also get the platform via Anaconda. Once you have downloaded the setup to the desktop, the next step is to install it. For that you need to update the setup tools and run the python -m pip install -U pip
Installing Python in Mac OS X devices is even easier, you simply have to go to the official website of Python and get the program through a .dmg package. We would also suggest the homebrew platform that is far more dependable and risk-free.
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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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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
Its location on the Wicomico River and proximity to major cities like Baltimore, Washington D.C, Philadelphia and Norfolk have contributed significantly to its growth. Even in the age of the early settlers, the port of Salisbury was used extensively for trade and commerce. Today it has business interests in poultry, electronics, manufacturing, agriculture and shipbuilding. Education is also a primary sector with Salisbury University being among the top employers. Other nationals and multinationals who have made Salisbury their home include Verizon, Pepsi, The Knowland group and others. The recent cleaning and revitalization of many of the city?s neighbourhoods and the adjoining areas has given it a new lease of life. It is now reinventing itself as a cultural center promoting, music, dance, and various festivals. The city also maintains several parks and playgrounds which make it a good place to unwind. Professionals seeking employment here would do well with certifications such as PRINCE2, PMP, PMI-ACP, CSM, CEH, CSPO, Scrum & Agile, MS courses and others. Note: Please note that the actual venue may change according to convenience, and will be communicated after the registration.