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Data Science with Python Training in Baltimore, MD, United States

Learn to analyze data with Python in this Data Science with Python comprehensive course.

  • 42 hours of Instructor led Training
  • Interactive Statistical Learning with advanced Excel
  • Comprehensive Hands-on with Python
  • Covers Advanced Statistics and Predictive Modeling
  • Learn Supervised and Unsupervised Machine Learning Algorithms

Live Online Classroom (Weekend)

Apr 10 - May 23 08:00 PM - 11:00 PM ( EDT )

USD 2199

USD 1899

Live Online Classroom (Weekday)

Apr 12 - May 10 08:00 PM - 10:00 PM ( EDT )

USD 2199

USD 1899

CITREP+ funding support is eligible for Singapore Citizens and Permanent Residents


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.

What You Will Learn


There are no prerequisites to attend this course, but elementary programming knowledge will come in handy.

3 Months FREE Access to all our E-learning courses when you buy any course with us

Who should Attend?

  • Those Interested in the field of data science
  • Those looking for a more robust, structured Python learning program
  • Those wanting to use Python for effective analysis of large datasets
  • Software or Data Engineers interested in quantitative analysis with Python
  • Data Analysts, Economists or Researchers

KnowledgeHut Experience

Instructor-led Live Classroom

Interact with instructors in real-time— listen, learn, question and apply. Our instructors are industry experts and deliver hands-on learning.

Curriculum Designed by Experts

Our courseware is always current and updated with the latest tech advancements. Stay globally relevant and empower yourself with the training.

Learn through Doing

Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.

Mentored by Industry Leaders

Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.

Advance from the Basics

Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.

Code Reviews by Professionals

Get reviews and feedback on your final projects from professional developers.


Learning Objectives:

Get an idea of what data science really is.Get acquainted with various analysis and visualization tools used in  data science.

Topics Covered:

  • What is Data Science?
  • Analytics Landscape
  • Life Cycle of a Data Science Project
  • Data Science Tools & Technologies

Hands-on:  No hands-on

Learning Objectives:

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.

Topics Covered:

  • Python Basics
  • Data Structures in Python
  • Control & Loop Statements in Python
  • Functions & Classes in Python
  • Working with Data
  • Analyze Data using Pandas
  • Visualize Data 
  • Case Study


  • Know how to install Python distribution like Anaconda and other libraries.
  • Write python code for defining your own functions,and also learn to write object oriented way of writing classes and objects. 
  • Write python code to import dataset into python notebook.
  • Write Python code to implement Data Manipulation, Preparation & Exploratory Data Analysis in a dataset.

Learning Objectives: 

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.

Topics Covered:

  • Measures of Central Tendency
  • Measures of Dispersion
  • Descriptive Statistics
  • Probability Basics
  • Marginal Probability
  • Bayes Theorem
  • Probability Distributions
  • Hypothesis Testing 


Write python code to formulate Hypothesis and perform Hypothesis Testing on a real production plant scenario

Learning Objectives: 

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.

Topics Covered:

  • Linear Regression (OLS)
  • Case Study: Linear Regression
  • Principal Component Analysis
  • Factor Analysis
  • Case Study: PCA/FA


  • With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.
  • Reduce Data Dimensionality for a House Attribute Dataset for more insights & better modeling.

Learning Objectives: 

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.

Topics Covered:

  • Logistic Regression
  • Case Study: Logistic Regression
  • K-Nearest Neighbor Algorithm
  • Case Study: K-Nearest Neighbor Algorithm
  • Decision Tree
  • Case Study: Decision Tree


  • With various customer attributes describing customer characteristics, build a classification model to predict which customer is likely to default a credit card payment next month. This can help the bank be proactive in collecting dues.

  • Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.

  • Wine comes in various types. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).

Learning Objectives:

Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data. Work on a real- life Case Study with ARIMA.

Topics Covered:

  • Understand Time Series Data
  • Visualizing Time Series Components
  • Exponential Smoothing
  • Holt's Model
  • Holt-Winter's Model
  • Case Study: Time Series Modeling on Stock Price


  • Write python code to Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data.
  • Write python code to Use Holt's model when your data has Constant Data, Trend Data and Seasonal Data. How to select the right smoothing constants.
  • Write Python code to Use Auto Regressive Integrated Moving Average Model for building Time Series Model
  • Dataset including features such as symbol, date, close, adj_close, volume of a stock. This data will exhibit characteristics of a time series data. We will use ARIMA to predict the stock prices.

Learning Objectives:

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.

Topics Covered:

  • Industry relevant capstone project under experienced industry-expert mentor


 Project to be selected by candidates.


Predict House Price using Linear Regression

With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.

Predict credit card defaulter using Logistic Regression

This project involves building a classification model.

Read More

Predict chronic kidney disease using KNN

Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.

Predict quality of Wine using Decision Tree

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).

Note:These were the projects undertaken by students from previous batches. 

Data Science with Python

What is Data Science

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:

  1. Data scientists are always in high demand and hence very handsomely paid 
  2. Data is fundamental to any decision-making process of corporate and commercial organizations
  3. Data science also has immense scope for research and further innovations. 
  4. It is a career that is profitable for both the professional at an individual level and the enterprise at a commercial level.

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:

  • Academic Qualifications 
  • Python Coding
  • R Programming
  • SQL Database and Coding
  • Hadoop Platform
  • Apache Spark 
  • Machine learning and AI 
  • Data Visualisation

  1. Academic Skills: Most data scientists have a Master's degree or Ph.D., some might even opt for virtual classes to learn a special skill like how to use Hadoop or Big Data querying. To get into data science, one must have a postgraduate degree in mathematics, astrophysics, AI, Data engineering, coding or other related fields. Furthermore, you should also be well-versed in handling projects. 
  2. Python Coding: Python is a basic and intuitive platform for beginners who want to develop simple and functional data sets. It is a versatile, flexible and scalable programming language that works well on all platforms and devices. Plus, the platform performs several other functions like integrating data from various sources, sorting unstructured data and gathering insights that make data analysis easier and far more effective. 
  3. R Programming: R programming is an analytical tool that is used by data scientists to curate, sort and store massive volumes of data into structured units and logical data sets. The platform enables quicker and hassle-free problem solving, faster analysis and yields accurate results from the insights thus gained. 
  4. Hadoop Platform: Mastering Hadoop is an added advantage for data scientists. It is not a mandatory requirement but it sure makes your job a lot easier. it is an open source cloud-based platform that allows data scientists to create separate data sets, sort data and create algorithms. 
  5. SQL and Coding: Mastering SQL database is a must for all data engineers and scientists. It is a platform specifically designed to help coders access, communicate and work on massive levels of data. It gives the user valuable insights into the structure of the data as well.
  6. Machine Learning and AI: AI is a field of study with a lot of scope and opportunity for innovation. Machine Learning, on the other hand, is something which all software engineers and technicians have to learn about. The concepts of AI and Machine learning give the data scientist a better hold over the data, allowing him to understand and optimize the results better. 
  7. Apache Spark: It is another popular data sharing technology which comes with a huge big data computational functionality and a robust interface. Also, unlike Hadoop, this platform is faster, better and more equipped to deal with massive data sets and unstructured data. Apache Spark also aids Data Scientists in preventing the loss of data. 
  8. Data Visualisation: There are several visualization tools like d3.js, Tableau, and ggplot where you can convert unstructured data into multiple formats, optimize it for multiple devices, and arrange it so as to yield maximum results. It also allows users to build complex algorithms and programs using this data, making it more comprehensible and easier to access. 

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 –

  • Passion: Like any other job, even data science requires a degree of passion and curiosity. You must have a love for learning, a great intellectual caliber for analyzing data and a keenly logical mind to figure out patterns.
  • Creativity: Data scientists also have to possess a degree of creativity to look for innovative solutions for problems. Your job would require some out-of-the-box thinking and quick problem-solving skills 
  • Perseverance: One must be hardworking and strong-willed to persist with a given project no matter how challenging it is. Data science often involves dealing with complicated data sets that don’t make sense and hence demands the professional to have enough determination to find meaning in it.
  • Patience: Remember, not all your solutions are bound to work. A lot of data science is about experimentation and exploration. One must have the maturity and patience to deal with failure and learn from mistakes and never lose heart.
  • Inventiveness: As a data scientist one must always find ways to tackle the given situation in new and unique ways. Hence, improvisation, flexibility, and inventiveness are crucial virtues for a good 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. 

  • Pay Scale: It is no hidden fact that data scientists get paid well, in fact, it is among the highest paying jobs in the world according to Glassdoor. And the fact that there is no dearth of demand in the IT industry for data scientists makes this a lucrative career option. 
  • The Respectability: Data scientists are highly respected in the IT sector. They usually hold prestigious degrees and PhDs for their research work and are hence considered to be experts in their respective fields.
  • Job Satisfaction: Being a data scientist is an intensely satisfying and exciting career opportunity. For starters, you won't be confined to your work desk all day like other software engineers. As a data scientist, your job would entail visiting sites, and you can travel around the world and attend conferences as well.  
  • Scope for Growth: Data science as a field is still developing and hence has scope for research and development. One can collaborate with fellow data scientists and work out new and innovative ways to build data sets, get better insights and new analytical perspectives. 

Data Scientist Skills and Qualifications

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: 

  • Managerial Skills: This involves setting up harmonious relations with other departments, organizing conferences, and managing tons of other things, as a data scientist one has to be good at multitasking. 
  • Technical Skills: Technical skills obviously play a crucial role in determining one’s career as a successful data scientist. One has to be well-versed in programming languages, platforms like Hadoop and Spark, and have a good idea about the advanced measures taken up by AI to improve data science.  
  • Communication Skills: As a data scientist you will have to deal with investors, clients, project heads and sometimes even the customers to gather the general trends and preferences of the market. This calls for some serious communication skills and a charming personality. 
  • Sharp Intellect: Intelligence and wit are also important prerequisites of the job which allows the person to think up innovative ideas and get quick and actionable solutions to complicated problems. 
  • Industry Knowledge: Lastly the data scientist must have in-depth knowledge of the industry he/she is associated with. This will help them to make important decisions and handle projects with the right awareness.

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:

  • Boot camps: Boot camps are organized for professionals who need to brush up their programming skills. Most of these camps last for 5 to 7 days and offer practical as well as theoretical knowledge to candidates. 
  • MOOC courses: MOOCs are online courses where data science experts and industry professionals from across the world give personal lectures and conferences to students. It is a perfect opportunity for tech enthusiasts to get some hands-on experience of what actually happens in the workspace. 
  • Certifications: There are several other courses which provide certifications that can add significant value and weight to your CV. These courses are delivered by professional and credible institutions and hence are trustworthy and a good investment.
  • Projects: Most data science courses offer project assignments giving you a practical experience of the industry as a part of your coursework. It allows you to explore the different sectors of the market, widen your contacts, and experiment with different platforms. 

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; 

  • Beginner Level
    • Iris Data Set: Suitable for recognising patterns and recurring market trends. This dataset has just 4 rows and 50 columns.
      Practice Problem: The problem is using these parameters to predict the class of the flowers. 
    • Loan Prediction Data Set: Applied in the banking sector for computing huge volumes of data. It is a classification problem dataset with 13 columns and 615 rows.
      Practice Problem: The problem is to predict if the loan will be approved or not. 
    • Bigmart Sales Data Set: Suitable for retail sector, to figure out customer trends and preferences. This dataset is a regression problem with 12 columns and 8523 rows.
      Practice Problem: The problem is predicting the sales of the retail store. 
  • Intermediate Level:
    • Black Friday Data Set: To understand and manipulate market trends in the retail sector. It is a regression problem with 12 columns and 550,069 rows.
      Practice Problem: The problem is predicting the total amount of purchase.
    • Human Activity Recognition Data Set: Ideal for recording, storing and analysing The dataset consists of 561 columns and 10,299 rows.
      Practice Problem: The problem is the prediction of the category of human activity. 
    • Text Mining Data Set: The data set is applied to the aviation sector and comes with 30,438 rows and 21,519 columns.
      Practice Problem: The problem is the classification of documents based on their labels. 
  • Advanced Level:
    • Urban Sound Classification: Consisting of 10 classes with 8,732 sound clippings of urban sounds, this problem introduces the developer to the audio processing in the real-world scenarios of classification.
      Practice Problem: The problem is the classification of the sound obtained from specific audio. 
    • Identify the digits data set: Consisting of 7000 images of 31 MB and 28X28 dimensions, this data set helps you in studying, analyzing, and recognizing elements present in a particular image.
      Practice Problem: The problem is identifying the digits present in an image. 
    • Vox Celebrity Data Set: Great for voice recognition, editing sound files. It contains 100,000 words spoken by 1,251 celebrities.
      Practice Problem: The problem is the identification of the voice of a celebrity.

How to Become a Data Scientist in Baltimore, Maryland

Here are the steps that you must follow in order to become a top-notch Data Scientist:

  • Pick an appropriate programming platform, we recommend R and Python 
  • Understand the intricate concepts of maths and stats 
  • Learn about data visualisation 
  • Master the concepts of AI and ML

Here’s what one needs to do in order to kick start their career in data science:

  • Get the appropriate degree or certification. Most data science candidates have masters degree or a PhD in data science 
  • You have to learn how to deal with unstructured data and arrange it logically
  • Understand and learn about the programming platforms where you can run the data sets. 
  • Train in ML and AI, which can be applied in data science 
  • Apply and implement data visualisation tools like ggplot etc. 

Here is how your degree helps in securing a good position in data science:

  • A degree course provides a set structure and helps you learn data science in a systematic manner. 
  • It enables you to widen your network and meet experts in the data science field
  • A degree in data science is a great addition to your CV
  • Candidates get better opportunities at getting lucrative internships 

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; 

  • Programming helps one deal with huge volumes of data sets and customise it effectively.
  • Coding allows you to calculate heavy data set quickly and without any hassles.
  • With programming languages, one can engage with data sets in a better and more optimised way  
  • It allows one to explore and experiment with data sets and figure out innovative solutions. 

Data Scientist Jobs in Baltimore, Maryland

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:

  • Pick a programming language and learn it thoroughly 
  • Brush on your math and stats 
  • Get access to extensive libraries online where you can practice data sets and learn more about data science. 
  • Learn about data visualisation platform 
  • Understanding concepts of ML and AI 
  • Execute these skills in real-time programs. 

Here is how one can prepare for a career in data science

  • Complete the required academic degree and get the appropriate certifications 
  • Attend meetings and conferences to stay updated with the latest developments in data science
  • Participate in contests and competitions where you can explore and experiment with code and data sets 
  • Apply for corporate companies and attend interviews to get experience and gain confidence 
  • Ensure that you have the referrals by your professors and previous employers

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:

  • To collect data (both structured and unstructured data) from varied and relevant sources, organize and analyze the collected data, and extract what’s important 
  • Create ML techniques, programs, and tools to make the data comprehensible
  • Create algorithms and stats to predict possible outcomes. 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:

  • Attend data science conferences, present your research papers and, connect with fellow data scientists in the process 
  • There are also online platforms like LinkedIn and other professional websites where you can post your CV. Corporate houses usually look for data scientists in these web platforms 
  • There are also social gatherings and, trade fairs you could attend to widen your contact circle 
  1. Data Scientist
  2. Data Analytics Manager
  3. Data Analyst
  4. Data Administrator
  5. Data Architect
  6. Business Analyst
  7. Business Intelligence Manager
  8. Marketing Analyst

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,, 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. 

Data Science With Python Baltimore, Maryland

Read on to know why Python is best for beginners; 

  • Python is an open source and flexible platform, the OOPS framework is easy and user-friendly.
  • The syntax is readable, comprehensive and intuitive 
  • Python offers an extensive range of resources and features 
  • The Python community is expansive, helpful and replete with help blogs and tutorials

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; 

  • Top-notch data packages, statistical analysis models, optimized templates,
  • The package is widely connected to multiple networks, over 8000 till date that ensures visibility and boosts performance. 
  • Viva GGPLOT, Visual tools for smooth matrix handling  

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: 

  • Open source platform offers greater flexibility and customization options
  • Comes with special features like Scikit learn, sensor flow and Pandas for quick and effective data analysis 

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:

  • Versatile, flexible, time-efficient and easy to handle 
  • Great for multitasking 

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; 

  • Java runs on OOPS framework, is optimized for all platforms and hence easy to customize. 
  • Users can edit and design codes for both frontend and backend applications 
  • Plus, it is easy to compile data using Java 

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; 

  • Scala supports Java and other OOPS platforms 
  • It is also used along Apache Spark and other high-performance programming languages.

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, 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. 

  • To install python you need to install brew and need the Apple Xcode package which can be procured using the $ Xcode-select –install command. 
  • An alternative way to Install brew package using /usr/bin/ruby -e "$
  • (curl -is"
  • Install the latest version of the program and ten confirm the version, 
  • We would also recommend installing the virtual.env which will help create separate programs and framework for different versions. 

reviews on our popular courses

Review image

The content was sufficient and the trainer was well-versed in the subject. Not only did he ensure that we understood the logic behind every step, he always used real-life examples to make it easier for us to understand. Moreover, he spent additional time to let us consult him on Data Science-related matters outside the curriculum. He gave us advice and extra study materials to enhance our understanding. Thanks, KnowledgeHut!

Ong Chu Feng

Data Analyst
Attended Data Science with Python Certification workshop in January 2020
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The trainer was really helpful and completed the syllabus on time and also provided live examples which helped me to remember the concepts. Now, I am in the process of completing the certification. Overall good experience.

Vito Dapice

Data Quality Manager
Attended PMP® Certification workshop in May 2018
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Knowledgehut is the best training institution. The advanced concepts and tasks during the course given by the trainer helped me to step up in my career. He used to ask for feedback every time and clear all the doubts.

Issy Basseri

Database Administrator
Attended PMP® Certification workshop in May 2018
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Everything was well organized. I would definitely refer their courses to my peers as well. The customer support was very interactive. As a small suggestion to the trainer, it will be better if we have discussions in the end like Q&A sessions.

Steffen Grigoletto

Senior Database Administrator
Attended PMP® Certification workshop in May 2018
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The course which I took from Knowledgehut was very useful and helped me to achieve my goal. The course was designed with advanced concepts and the tasks during the course given by the trainer helped me to step up in my career. I loved the way the technical and sales team handled everything. The course I took is worth the money.

Rosabelle Artuso

.NET Developer
Attended PMP® Certification workshop in May 2018
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The customer support was very interactive. The trainer took a very practical oriented session which is supporting me in my daily work. I learned many things in that session. Because of these training sessions, I would be able to sit for the exam with confidence.

Yancey Rosenkrantz

Senior Network System Administrator
Attended Agile and Scrum workshop in May 2018
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The Trainer at KnowledgeHut made sure to address all my doubts clearly. I was really impressed with the training and I was able to learn a lot of new things. I would certainly recommend it to my team.

Meg Gomes casseres

Database Administrator.
Attended PMP® Certification workshop in May 2018
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I feel Knowledgehut is one of the best training providers. Our trainer was a very knowledgeable person who cleared all our doubts with the best examples. He was kind and cooperative. The courseware was excellent and covered all concepts. Initially, I just had a basic knowledge of the subject but now I know each and every aspect clearly and got a good job offer as well. Thanks to Knowledgehut.

Archibold Corduas

Senior Web Administrator
Attended Agile and Scrum workshop in May 2018


The Course

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. 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.

  • Get advanced knowledge of data science and how to use them in real life business
  • Understand the statistics and probability of Data science
  • Get an understanding of data collection, data mining and machine learning
  • Learn tools like Python

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

  • Python
  • MS Excel

There are no restrictions but participants would benefit if they have basic programming knowledge and familiarity with statistics.

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. 

Finance Related

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.

KnowledgeHut offers a 100% money back guarantee if the candidate withdraws from the course right after the first session. To learn more about the 100% refund policy, visit our Refund Policy.

The Remote Experience

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

Have More Questions?

Data Science with Python Certification Course in Baltimore, MD

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.