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

Get hands-on Python skills and accelerate your data science career

  • Learn Python, analyze and visualize data with Pandas, Matplotlib and Scikit
  • Create robust predictive models with advanced statistics
  • Leverage hypothesis testing and inferential statistics for sound decision-making
  • 220,000 + Professionals Trained
  • 250 + Workshops every month
  • 70 + Countries and counting

Grow your Data Science skills

This comprehensive hands-on course takes you from the fundamentals of Data Science to an advanced level in weeks. Get hands-on programming experience in Python that you'll be able to immediately apply in the real world. Equip yourself with the skills you need to work with large data sets, build predictive models and tell a compelling story to stakeholders.

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Highlights

  • 42 Hours of Live Instructor-Led Sessions

  • 60 Hours of Assignments and MCQs

  • 36 Hours of Hands-On Practice

  • 6 Real-World Live Projects

  • Fundamentals to an Advanced Level

  • Code Reviews by Professionals

Data Scientists are in high demand across industries

data-science-with-python-certification-training

Data Science has bagged the top spot in LinkedIn’s Emerging Jobs Report for the last three years. Thousands of companies need team members who can transform data sets into strategic forecasts. Acquire in-demand data science and Python skills and meet that need.

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The KnowledgeHut Edge

Learn by Doing

Our immersive learning approach lets you learn by doing and acquire immediately applicable skills hands-on.

Real-World Focus

Learn theory backed by real-world practical case studies and exercises. Skill up and get productive from the get-go.

Industry Experts

Get trained by leading practitioners who share best practices from their experience across industries.

Curriculum Designed by the Best

Our Data Science advisory board regularly curates best practices to emphasize real-world relevance.

Continual Learning Support

Webinars, e-books, tutorials, articles, and interview questions - we're right by you in your learning journey!

Exclusive Post-Training Sessions

Six months of post-training mentor guidance to overcome challenges in your Data Science career.

Prerequisites

Prerequisites for the Data Science with Python training program

  • There are no prerequisites to attend this course.
  • Elementary programming knowledge will be of advantage.

Who should attend this course?

Professionals in the field of data science

Professionals looking for a robust, structured Python learning program

Professionals working with large datasets

Software or data engineers interested in quantitative analysis

Data analysts, economists, researchers

Data Science with Python Course Schedules

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What you will learn in the Data Science with Python course

1

Python Distribution

Anaconda, basic data types, strings, regular expressions, data structures, loops, and control statements.

2

User-defined functions in Python

Lambda function and the object-oriented way of writing classes and objects.

3

Datasets and manipulation

Importing datasets into Python, writing outputs and data analysis using Pandas library.

4

Probability and Statistics

Data values, data distribution, conditional probability, and hypothesis testing.

5

Advanced Statistics

Analysis of variance, linear regression, model building, dimensionality reduction techniques.

6

Predictive Modelling

Evaluation of model parameters, model performance, and classification problems.

7

Time Series Forecasting

Time Series data, its components and tools.

Skill you will gain with the Data Science with Python course

Python programming skills

Manipulating and analysing data using Pandas library

Data visualization with Matplotlib, Seaborn, ggplot

Data distribution: variance, standard deviation, more

Calculating conditional probability via hypothesis testing

Analysis of Variance (ANOVA)

Building linear regression models

Using Dimensionality Reduction Technique

Building Binomial Logistic Regression models

Building KNN algorithm models to find the optimum value of K

Building Decision Tree models for regression and classification

Visualizing Time Series data and components

Exponential smoothing

Evaluating model parameters

Measuring performance metrics

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Invest in forward-thinking data talent to leverage data’s predictive power, craft smart business strategies, and drive informed decision-making.

  • Immersive Learning with a Learn-by-Doing approach.
  • Applied Learning to get your teams project-ready.
  • Align skill development to your most important objectives.
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Data Science with Python Course Curriculum

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Learning objectives
Understand the basics of Data Science and gauge the current landscape and opportunities. Get acquainted with various analysis and visualization tools used in data science.


Topics

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

Learning objectives
The Python module will equip you with a wide range of Python skills. You will learn to:

  • To Install Python Distribution - Anaconda, basic data types, strings, and regular expressions, data structures and loops, and control statements that are used in Python
  • To write user-defined functions in Python
  • About Lambda function and the object-oriented way of writing classes and objects 
  • How to import datasets into Python
  • How to write output into files from Python, manipulate and analyse data using Pandas library
  • Use Python libraries like Matplotlib, Seaborn, and ggplot for data visualization

Topics

  • Python Basics
  • Data Structures in Python 
  • Control and Loop Statements in Python
  • Functions and Classes in Python
  • Working with Data
  • Data Analysis using Pandas
  • Data Visualisation
  • Case Study

Hands-on

  • How to install Python distribution such as Anaconda and other libraries
  • To write python code for defining as well as executing your own functions
  • The object-oriented way of writing classes and objects
  • How to write python code to import dataset into python notebook
  • How to write Python code to implement Data Manipulation, Preparation, and Exploratory Data Analysis in a dataset

Learning objectives
In the Probability and Statistics module you will learn:

  • Basics of data-driven values - mean, median, and mode
  • Distribution of data in terms of variance, standard deviation, interquartile range
  • Basic summaries of data and measures and simple graphical analysis
  • Basics of probability with real-time examples
  • Marginal probability, and its crucial role in data science
  • Bayes’ theorem and how to use it to calculate conditional probability via Hypothesis Testing
  • Alternate and Null hypothesis - Type1 error, Type2 error, Statistical Power, and p-value

Topics

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

Hands-on

  • How to write Python code to formulate Hypothesis
  • How to perform Hypothesis Testing on an existent production plant scenario

Learning objectives
Explore the various approaches to predictive modelling and dive deep into advanced statistics:

  • Analysis of Variance (ANOVA) and its practicality
  • Linear Regression with Ordinary Least Square Estimate to predict a continuous variable
  • Model building, evaluating model parameters, and measuring performance metrics on Test and Validation set
  • How to enhance model performance by means of various steps via processes such as feature engineering, and regularisation
  • Linear Regression through a real-life case study
  • Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis
  • Various techniques to find the optimum number of components or factors using screen plot and one-eigenvalue criterion, in addition to a real-Life case study with PCA and FA.

Topics

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

Hands-on

  • With attributes describing various aspect of residential homes for which you are required to build a regression model to predict the property prices
  • Reducing Dimensionality of a House Attribute Dataset to achieve more insights and better modelling

Learning objectives
Take your advanced statistics and predictive modelling skills to the next level in this advanced module covering:

  • Binomial Logistic Regression for Binomial Classification Problems
  • Evaluation of model parameters
  • Model performance using various metrics like sensitivity, specificity, precision, recall, ROC Curve, AUC, KS-Statistics, and Kappa Value
  • Binomial Logistic Regression with a real-life case Study
  • KNN Algorithm for Classification Problem and techniques that are used to find the optimum value for K
  • KNN through a real-life case study
  • Decision Trees - for both regression and classification problem
  • Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID
  • Using Decision Tree with real-life Case Study

Topics

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

Hands-on

  • Building a classification model to predict which customer is likely to default a credit card payment next month, based on various customer attributes describing customer characteristics
  • Predicting if a patient is likely to get any chronic kidney disease depending on the health metrics
  • Building a model to predict the Wine Quality using Decision Tree based on the ingredients’ composition

Learning objectives
All you need to know to work with time series data with practical case studies and hands-on exercises. You will:

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

Topics

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

Hands-on

  • Writing python code to Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data.
  • Writing 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.
  • Writing Python code to Use Auto Regressive Integrated Moving Average Model for building Time Series Model
  • Use ARIMA to predict the stock prices based on the dataset including features such as symbol, date, close, adjusted closing, and volume of a stock.

Learning objectives
This industry-relevant capstone project under the experienced guidance of an industry expert is the cornerstone of this Data Science with Python course. In this immersive learning mentor-guided live group project, you will go about executing the data science project as you would any business problem in the real-world.


Hands-on

  • Project to be selected by candidates.

FAQs on the Data Science with Python Course

Data Science with Python Training

The Data Science with Python course has been thoughtfully designed to make you a dependable Data Scientist ready to take on significant roles in top tech companies. At the end of the course, you will be able to:

  • Build Python programs: distribution, user-defined functions, importing datasets and more
  • Manipulate and analyse data using Pandas library
  • Data visualization with Python libraries: Matplotlib, Seaborn, and ggplot
  • Distribution of data: variance, standard deviation, interquartile range
  • Calculating conditional probability via Hypothesis Testing
  • Analysis of Variance (ANOVA)
  • Building linear regression models, evaluating model parameters, and measuring performance metrics
  • Using Dimensionality Reduction Technique
  • Building Binomial Logistic Regression models, evaluating model parameters, and measuring performance metrics
  • Building KNN algorithm models to find the optimum value of K
  • Building Decision Tree models for both regression and classification problems
  • Build Python programs: distribution, user-defined functions, importing datasets and more
  • Manipulate and analyse data using Pandas library
  • Visualize data with Python libraries: Matplotlib, Seaborn, and ggplot
  • Build data distribution models: variance, standard deviation, interquartile range
  • Calculate conditional probability via Hypothesis Testing
  • Perform analysis of variance (ANOVA)
  • Build linear regression models, evaluate model parameters, and measure performance metrics
  • Use Dimensionality Reduction
  • Build Logistic Regression models, evaluate model parameters, and measure performance metrics
  • Perform K-means Clustering and Hierarchical Clustering
  • Build KNN algorithm models to find the optimum value of K
  • Build Decision Tree models for both regression and classification problems
  • Build data visualization models for Time Series data and components
  • Perform exponential smoothing

The program is designed to suit all levels of Data Science expertise. From the fundamentals to the advanced concepts in Data Science, the course covers everything you need to know, whether you’re a novice or an expert. To facilitate development of immediately applicable skills, the training adopts an applied learning approach with instructor-led training, hands-on exercises, projects, and activities.

Yes, our Data Science with Python course is designed to offer flexibility for you to upskill as per your convenience. We have both weekday and weekend batches to accommodate your current job.

In addition to the training hours, we recommend spending about 2 hours every day, for the duration of course.

The Data Science with Python course is ideal for:

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

There are no prerequisites for attending this course, however prior knowledge of elementary programming, preferably using Python, would prove to be handy.

To attend the Data Science with Python training program, the basic hardware and software requirements are as mentioned below -

Hardware requirements

  • Windows 8 / Windows 10 OS, MAC OS >=10, Ubuntu >= 16 or latest version of other popular Linux flavors
  • 4 GB RAM
  • 10 GB of free space

Software Requirements

  • Web browser such as Google Chrome, Microsoft Edge, or Firefox

System Requirements

  • 32 or 64-bit Operating System
  • 8 GB of RAM

On adequately completing all aspects of the Data Science with Python course, you will be offered a course completion certificate from KnowledgeHut.

In addition, you will get to showcase your newly acquired data-handling and programming skills by working on live projects, thus, adding value to your portfolio. The assignments and module-level projects further enrich your learning experience. You also get the opportunity to practice your new knowledge and skillset on independent capstone projects.

By the end of the course, you will have the opportunity to work on a capstone project. The project is based on real-life scenarios and carried-out under the guidance of industry experts. You will go about it the same way you would execute a data science project in the real business world.

Data Science with Python Workshop

The Data Science with Python workshop at KnowledgeHut is delivered through PRISM, our immersive learning experience platform, via live and interactive instructor-led training sessions.

Listen, learn, ask questions, and get all your doubts clarified from your instructor, who is an experienced Data Science and Machine Learning industry expert.

The Data Science with Python course is delivered by leading practitioners who bring trending, best practices, and case studies from their experience to the live, interactive training sessions. The instructors are industry-recognized experts with over 10 years of experience in Data Science. 

The instructors will not only impart conceptual knowledge but end-to-end mentorship too, with hands-on guidance on the real-world projects.

Our Date Science course focuses on engaging interaction. Most class time is dedicated to fun hands-on exercises, lively discussions, case studies and team collaboration, all facilitated by an instructor who is an industry expert. The focus is on developing immediately applicable skills to real-world problems.

Such a workshop structure enables us to deliver an applied learning experience. This reputable workshop structure has worked well with thousands of engineers, whom we have helped upskill, over the years. 

Our Data Science with Python workshops are currently held online. So, anyone with a stable internet, from anywhere across the world, can access the course and benefit from it.

Schedules for our upcoming workshops in Data Science with Python can be found here.

We currently use the Zoom platform for video conferencing. We will also be adding more integrations with Webex and Microsoft Teams. However, all the sessions and recordings will be available right from within our learning platform. Learners will not have to wait for any notifications or links or install any additional software.

You will receive a registration link from PRISM to your e-mail id. You will have to visit the link and set your password. After which, you can log in to our Immersive Learning Experience platform and start your educational journey.

Yes, there are other participants who actively participate in the class. They remotely attend online training from office, home, or any place of their choosing.

In case of any queries, our support team is available to you 24/7 via the Help and Support section on PRISM. You can also reach out to your workshop manager via group messenger.

If you miss a class, you can access the class recordings from PRISM at any time. At the beginning of every session, there will be a 10-12-minute recapitulation of the previous class.

Should you have any more questions, please raise a ticket or email us at support@knowledgehut.com and we will be happy to get back to you.

What Learners Are Saying

O
Ong Chu Feng Data Analyst
4
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!

Attended Data Science with Python Certification workshop in January 2020

V
Vito Dapice Data Quality Manager
5

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.

Attended PMP® Certification workshop in April 2020

M
Mirelle Takata Network Systems Administrator
5

My special thanks to the trainer for his dedication and patience. I learned many things from him. I would also thank the support team for their help. It was well-organised, great work Knowledgehut team!

Attended Certified ScrumMaster (CSM)® workshop in July 2020

A
Astrid Corduas Telecommunications Specialist
5

The instructor was very knowledgeable, the course was structured very well. I would like to sincerely thank the customer support team for extending their support at every step. They were always ready to help and smoothed out the whole process.

Attended Agile and Scrum workshop in June 2020

E
Ellsworth Bock Senior System Architect
5

It is always great to talk about Knowledgehut. I liked the way they supported me until I got certified. I would like to extend my appreciation for the support given throughout the training. My trainer was very knowledgeable and I liked the way of teaching. My special thanks to the trainer for his dedication and patience.

Attended Certified ScrumMaster (CSM)® workshop in February 2020

A
Archibold Corduas Senior Web Administrator
5

The teaching methods followed by Knowledgehut is really unique. The best thing is that I missed a few of the topics, and even then the trainer took the pain of taking me through those topics in the next session. I really look forward to joining KnowledgeHut soon for another training session.

Attended Certified ScrumMaster (CSM)® workshop in May 2020

A
Alexandr Waldroop Data Architect.
5

The workshop held at KnowledgeHut last week was very interesting. I have never come across such workshops in my career. The course materials were designed very well with all the instructions were precise and comprehenisve. Thanks to KnowledgeHut. Looking forward to more such workshops.

Attended Certified ScrumMaster (CSM)® workshop in January 2020

T
Tilly Grigoletto Solutions Architect.
5

I really enjoyed the training session and am extremely satisfied. All my doubts on the topics were cleared with live examples. KnowledgeHut has got the best trainers in the education industry. Overall the session was a great experience.

Attended Agile and Scrum workshop in February 2020

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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.

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:

  • 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, 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. 

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, 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. 

  • 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 https://raw.githubusercontent.com/Homebrew/install/master/install)"
  • 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. 

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.

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