Data Science with Python Training in Atlanta, GA, United States

Atlanta, Georgia is home to several renowned institutions like Georgia Institute of Technology, Georgia State University that offers Master’s degree in Data Science. There are also other options available like certification courses and bootcamp, that will help you learn at your own pace.

A qualified data scientist is expected to be an expert in the following technical skills - 

1. Python Coding: Python is undoubtedly one of the most popular and preferred programming languages used in the data science field. It can take various data formats and aids in the data preprocessing. It is very simple and versatile that gives it an advantage over other programming languages. It also allows data scientists to create and perform operations on dataset. 
2. R Programming: If you want to make a data science problem easy to solve, you need to have the knowledge of R programming. To become a master data scientist, you need to have a comprehensive knowledge of this analytical tool. 
3. Hadoop Platform: Knowledge of Hadoop platform is not a must for data science, but it is used in several data science projects. So, it will be better if you have knowledge of the Hadoop platform. 
4. SQL database and coding: SQL, or Structured Query Language, is used by the data scientist for accessing, working, and communicating data. This helps the data scientist gain an understanding of the structure and formation of a database. MySQL is another such language with concise commands that saves time and does not require an expert technical skill level for performing operations on the database. 
5. Machine Learning and Artificial Intelligence: To be a successful data scientist, one needs to be proficient in Machine Learning and Artificial Intelligence. As a potential data scientist, you must make yourself efficient with topics like neural networks, decision trees, logistic regression, reinforcement learning, adversarial learning, machine learning algorithms, etc. 
6. Apache Spark: Used as a data sharing technology, Apache Spark is like Hadoop where it is used for big computation. It is faster than Hadoop. This is because Spark caches its computations in the memory of the system whereas Hadoop reads and writes to the disk. So, it is used for running the data science algorithms faster. Apache Spark also helps in disseminating data processing. It is well equipped to deal with large datasets and handle complex unstructured data. Unlike Hadoop, it can prevent loss of data. The most important factor of why it is so preferred in the field of Data Science is the speed and ease with which it allows the data scientists to carry out the project. 
7. Data Visualization: Once the data has been analyzed, a data scientist has to be able to present this in a form that is understandable to the non-technical members of the team. There are several visualization tools available for this purpose like Tableau, ggplot, d3.js, and matplotlib. Complex results are converted into a format that is easy to understand and comprehend. These results are obtained after a series of processes performed on a dataset. Data visualization also allows the organization to work with data. Data scientists can easily grasp the insights from the data and act on the new outcome. 
8. Unstructured data: Most of the data that is generated is unlabelled, not organized into databases values, and unstructured. This unstructured data includes blog posts, audio samples, videos, customer reviews, social media posts, etc. 

Below are the top 5 essential behavioral traits of a successful Data Science professional-

• Curiosity – If you live for 3Ws like Why What and Where, then data science is for you. 
• Inventiveness - Creativity in data science can be anything from discovering innovative ways to deal with data to develop new tools. You ought to presumably get a handle on what's missing and what ought to be joined to get results. 

A lot of local companies in Atlanta, GA have started using data science to help in improving the efficiency and optimizing their business. Companies like Aderant, Sparity Inc, Neudesic, Nexidia, Innovative architects, BI Brainz, PredictX, Keystone Solutions etc, are currently hiring data scientists. So, needless to say, data scientists will be in demand in Atlanta, GA. Below are some other top advantages of being a Data Scientist -  

1. High Pay: As a result of high demand and low supply, data science career is paying better as compared to other choices.
2. Huge rewards: Despite higher pay, data scientists can expect huge bonuses. 
3. Training: You need to have a Master's degree or a Ph.D. to become a data scientist. There is a huge demand for knowledge in this field. So, you can try getting a job as a lecturer or a researcher in a government or private institution.
4. Mobility: A data scientist job can get you a job in one of the developed countries, which means hefty salary and improved living standards. 
5. Network: A data scientist gets to network with other professionals in the tech world through conferences, tech talks, and other platforms. 

These are the must-have business skills to become a data scientist: 

1. Analytic Problem-Solving – You must be able to apply your problem-solving skills to derive a conclusion. Decision making requires critical thinking and analytical solutions. Problem-solving skills are required to apply a useful framework to solve a business problem and to determine which analytical method to apply given the nature of the problem and available data
2. Communication Skills – It is the responsibility of a data scientist to help the business communicate deep business and customer analytics.
3. Business acumen  – The main goal of a data scientist is to translate business problems into data science solutions through the implementation of data science skills. Therefore, it is important to understand the business requirements of the organization you are working with. You must understand how your solutions affect the business on a broader scale. You must understand how your business operates and how these techniques will be applied in real time so that your solutions fit accordingly. This lets you categorize the problems on the grounds of priority. 
4. Industry Knowledge – A good data scientist has a solid knowledge of the industry he/she is working in. This will help you in analyzing the data as you will know what is important and what is not.

• Training camps: Training camps are the ideal pathway to embark on your Python journey. In Atlanta, GA, a number of institutions organize boot camps to help the students kick start their career as a Data Scientist. DigitalCrafts: Atlanta Coding Bootcamp and Georgia Tech Data Science and Analytics boot camp are some examples of boot camps in Atlanta.
• MOOC courses: These are online courses which teach the most recent models in the business.
• Affirmations: The next step is to get some certifications that will help you build your portfolio. Here are some of the data science certifications that you can go for:
• Cloudera Certified Associate - Data Analyst
• Cloudera Certified Professional: CCP Data Engineer
• Applied AI with Deep Learning, IBM Watson IoT Data Science Certificate
• Projects: The more projects you work on, more refined your skills and thinking will be. You need to find new solutions to already solved problems while following the project constraints.
• Competitions: You can try participating in competitions like Kaggle that improves your problem-solving skills by making you find a solution that fulfills all the requirements. 

We live in a world of data. And now slowly, different industries have started to realize the importance and benefits of using data science for optimizing their business. Atlanta, GA is home to many such companies that are hiring data scientists either for their own or to use as a third party solution including TopRight, Stridely solutions, HatchWorks Technologies, Quatrro, Six Consulting, Inc., 7Factor Software, Aderant, Sparity Inc, Neudesic, Nexidia, Innovative architects, BI Brainz, PredictX, Keystone Solutions, etc.

Practicing and working is the best way to master any skills. Similarly, you need to work your way through the data science problem as well. Here are a few ways categorized on the basis of difficulty and expertise level:

• Beginner Level
  • Iris Data Set: This dataset consists of 4 columns and 50 rows. It is one of the most popular, easy, resourceful, and versatile datasets available for pattern recognition. You will be able to learn various classification techniques while dealing with this dataset. This is a great dataset for beginners to start in the field of data science.Practice Problem: Use the parameters to predict the class of a flower.
  • Loan Prediction Data Set: Consisting of 13 columns and 615 rows, this dataset is from the banking domain which uses more data science methodologies and data analytics than any other industry. With the loan prediction dataset, you will have to work with concepts used in the banking and insurance domain like strategies implemented, variables that can affect the outcome, and challenges faced. It is a classification problem dataset.Practice Problem: Predicting if a certain load will be approved or not.
• Intermediate Level:
  • Black Friday Data Set: This is a large dataset with 12 columns and 550,069 rows. This dataset is a regression problem consisting of sales transactions of customers in a retail store. If you want to understand the daily shopping experience of millions of customers and at the same time explore and expand your engineering skills, this is an apt dataset for you. Practice Problem: The problem is predicting the total purchase amount.
  • Human Activity Recognition Data Set: Consisting of 561 columns and 10,299 rows, the human activity data set was collected from 30 human subjects via smartphone’s recordings. These smartphones were embedded with inertial sensors.Practice Problem: The problem is predicting the category of human activity.
  • Text Mining Data Set: With 30,438 rows and 21,219 columns, this data set is a multi-classification, high dimensional problem dataset that was collected during the 2007's Siam Text Mining competition. The dataset had safety reports of aviation describing problems that occurred during the flights. Practice Problem: The problem is using the labels to classify the documents.
• Advanced Level:
  • Urban Sound Classification: There are several basic and simple machine learning problems like the Titanic survival prediction that you will study in the beginning. But these problems do not help you get familiar with the real world problems. This is where urban sound classification comes in. It will help you implement machine learning skills to real-world problems. It also includes the use of audio processing for real-world scenarios of classification. It has 8,732 sound clippings belonging to 10 different classes. Practice Problem: The problem is identifying particular audio and classifying it to its class.
  • Vox Celebrity Data Set: This dataset introduces you to the usage of audio processing in deep learning. This dataset is a large scale speaker identification problem consisting of 100,000 words spoken by 1,251 celebrities. This dataset is extracted from YouTube videos. It is a great example to practice isolating and identifying speech.Practice Problem: The problem is identifying the voice of the celebrity.

1. Getting started: The first step is to select a programming language to work with. We recommend that you pick either Python or R as they are the most popular languages used in the field of Data Science.
2. Mathematics and statistics: A good data scientist must have a good grasp of basic algebra and statistics. You will need them while dealing with data, discovering patterns and relationships.
3. Data visualization: Learning to visualize the data is an important step in becoming a data scientist. You need it for better communication with the end users and helping the non-technical members of the team understand the content as well.
4. ML and Deep learning: Every data scientist must be an expert in Deep Learning as well as Machine Learning. It helps you to analyze the data.

• Degree/certificate: There are several institutions in Atlanta, GA that offer Master’s degree in Data Science. It is important that you start with covering your fundamentals through a basic course. This can be either an online or an offline course. You will be able to learn the application of cutting-edge tools that will help you get a tremendous career growth. The field of Data Science demands continuous learning due to the rapid advancements. So, Data Scientists have more PhDs than any other job in the IT industry. 
• Unstructured data: The most important part of the roles of a Data Scientist is the identification of patterns in the data. Usually this data is unstructured and can’t be fit into a database. Structuring this data takes a lot of work and makes the job a lot more complex. As a data scientist, you must have the ability to understand and manipulate the data.  
• Software and Frameworks: As a Data Scientist, you would have to deal with large volumes of unstructured data. For this, you need to be comfortable in using a programming language, software and frameworks involved in Data Science. 
  • R is one such programming language that has a steep learning curve. It is one of the most used programming languages for finding solutions to statistical problems. It is the preferred language for the analysis of about 43% of Data Scientists. 
  • When the amount of data to be analyzed is way too large as compared to the available memory, the majority of Data Scientists use the Hadoop framework. Hadoop is capable of conveying data to various points on the machine. After Hadoop, Spark is a popular choice of framework. It is a faster option for computational work. Unlike Hadoop, it prevents the loss of data. 
  • Once you have a complete understanding of the programming language and the framework, you need to get a thorough knowledge of databases. A Data Scientist must be proficient in SQL queries. 
• Machine learning and Deep Learning: Once you have collected and prepared the data, the next step is the application of Machine Learning algorithms on the data for the analysis. Deep learning is used to train the model to work with the provided data.
• Data visualization: Most of the data science projects involve making business decisions after analyzing and visualizing the data. It is the job of a Data Scientist to analyze the data and provide it to the management in the form of charts and graphs. There are several tools available for data visualization like ggplot2, matplotlib, etc. 

The importance of degree in the field is summarized below:

• Networking: Networking is important in all fields and it can be developed while pursuing degrees.
• Structured education: Having a structured curriculum and a schedule to follow is always beneficial.
• Internships: These allow much needed practical experience
• Qualification for CVs: Earning a degree from a reputed institution is always helpful for your career.

There are many colleges in Atlanta, GA that offer Master’s degree in Data Science like Georgia Institute of Technology and Georgia State University. However, before selecting the college, you need to figure out if you need a master’s degree or not. The most ideal approach to decide if you need a Masters in Data Science is by reviewing yourself on the scorecard underneath. The given scorecard can help you figure that out. You should get the degree if you get over 6 points in total: 

• A strong background in STEM (Science/Technology/Engineering/Management)- 0 point.
• Weak STEM background, such as biochemistry, biology, economics, etc.- 2 points
• Non-STEM background- 5 points
• Python programming experience less than 1 year in total- 3 points
• No job experience in coding- 3 points
• Lack of capability to learn independently- 4 points
• Not understanding that this scorecard follows a regression algorithm- 1 point.

Knowledge of programming is the most fundamental and important skills required to become a data scientist. Here is why:

• Data sets: Working in data science involves dealing with huge volumes of datasets. To analyze these datasets, knowledge of programming is essential.
• Statistics: Knowledge of statistics is a must to analyze the data. But what good would be this knowledge if you don’t have the programming knowledge to implement it? 
• Framework: If you are proficient in programming, you will be able to build systems and frameworks that can help the organization automatically analyze experiments. This is also required to maintain the data pipeline and visualize the data.

A Data Scientist in Atlanta gets an average remuneration of $88,603 per year.

The annual income of data scientist in Atlanta and New York is $88,603 and $99,716 respectively, with a difference of $11,113.

The data scientists earn an average of $88,603 in Atlanta as compared to $110,925 in Chicago.

The average income of a Data Scientist is $77,379 per year in Jersey, Georgia.

In Georgia, the demand for a Data Scientist is quite high. There are several firms that have just started using Data Science and are looking to build a Data Science team that will help them make better business decisions by analyzing their raw data.

Following are the benefits of being a Data Scientist in Atlanta:High SalaryJob growthMultiple job opportunityChance of being an integral part of the team from the start

Data Scientists have a few perks and advantages over other jobs. More often than not, they get to gain attention of the upper level management due to their involvement in getting business insights after the analysis of raw data. Also, they get to pick their field of work as Data Science has spread its roots in almost every field that exists.

Cox Automotive Inc., Charter Global and Norfolk Southern Corporation are among the companies hiring Data Scientists in Atlanta.

1. Data Science Atl Conference 2019 || #DSATLConf19, Atlanta

• About the conference: The conference is going to explain the applications of advanced data science in various technical disciplines.
• Event Date: 17 Oct, 2019 to 18 Oct, 2019
• Venue: The Historic Academy of Medicine at GaTech 875 West Peachtree Street Northwest Atlanta, GA 30309 United States
• Days of Program: 2
• Timings: Thu, Oct 17, 2019, 8:00 AM – Fri, Oct 18, 2019, 6:00 PM EDT
• Purpose: The purpose of the conference is to share and inspire new algorithms, techniques, and applications.
• Registration cost: $199 – $549
• Who are the major sponsors: Data Science Connect

2. Free Thinkful Webinar | Web Development vs Data Science, Atlanta

• About the conference: This conference will help you break down the specifics of web development and Data Science. You will be able to better understand which one suits you the best.
• Event Date: May 7, 2019
• Venue: Thinkful Webinar Online Atlanta, GA United States
• Days of Program: 1
• Timings: 12:30 PM – 2:00 PM EDT
• Purpose: The purpose of the conference is to get the basic information regarding the skills required to be successful in either of these fields.
• Registration cost: Free
• Who are the major sponsors: Thinkful Atlanta

3. Chris Hyde: An Introduction to Data Science With Python, Atlanta

• About this conference: This conference will give data professionals a kick start in the field of Data Science. The seminar will also include the use of Python programming language in data science and its basic methodologies.
• Event Date: May 17, 2019
• Venue: Microsoft Office - Alpharetta 8000 Avalon Boulevard #Suite 900 Alpharetta, GA 30009 United States
• Days of Program: 1
• Timings: 8:30 AM – 4:30 PM EDT
• Purpose: The purpose of the seminar is to help professionals understand the basic statistics around the data and create simple visualizations for identifying patterns and trends.
• With whom can you network in this Conference: You will be able to network with DBAs, Database developers, Data analysts and basically anyone looking to get started with Data Science.
• Registration cost: $150
• Who are the major sponsors: SQL Saturday 845, presented by Atlanta MDF

4. Data Science North Carolina Conference 2019 || #DSNCConf19, Atlanta

• About the conference: The conference will showcase the applications of data science across various industries.
• Event Date: 29 Aug, 2019 to 30 Aug, 2019
• Venue: 28223 United States
• Days of Program: 1
• Timings: Thu, Aug 29, 2019, 8:00 AM – Fri, Aug 30, 2019, 6:00 PM EDT
• Purpose: The purpose of the conference is to recognize the impact Data Science has on businesses.
• Registration cost: $199 – $549
• Who are the major sponsors: Data Science Connect

5. Data Connectors Atlanta Cybersecurity Conference 2019, Atlanta

• About the conference: The conference will have discussions on the use of Data Science to deal with Cyber criminals.
• Event Date: September 12, 2019
• Venue: Atlanta, GA United States
• Days of Program: 1
• Timings: 8:00 AM – 5:00 PM EDT
• Purpose: The purpose of the conference is to deal with cyber criminals using Data Science.
• Registration cost: Free
• Who are the major sponsors: Data Connectors

6. Angelbeat Technology Seminar on Cloud/Security/AI/Data, Atlanta

• About the conference: The conference will focus on the important Data Science areas like security, network, systems management, storage, mobility, wireless and collaboration, automation and orchestration, etc.
• Event Date: July 15, 2019
• Venue: Atlanta, GA
• Days of Program: 1
• Timings: 8:00 AM to 3:00 PM (EDT)
• Purpose: The purpose of this conference is to help the attendees understand the rapidly changing technologies that are data oriented and cloud centric.
• How many speakers: 1
• Speakers & Profile: Ron Gerber - Angelbeat CEO
• With whom can you network in this Conference: You will be able to network with professionals from Databases, Data Governance, security, storage, digital transformation, data analytics, AI/ML, programming, and DevOps.
• Registration cost: $200
• Who are the major sponsors: Angelbeat technology

7. Byte to Beautiful: Using GCP to Create an AI Driven Customer Experience, Atlanta

• About the conference: The conference will help you in collecting, aggregating, visualizing, and predicting the crucial business data.
• Event Date: June 4, 2019
• Venue: Atlanta, Ga, United States
• Days of Program: 1
• Timings: 11:30 AM – 1:30 PM EDT
• Purpose: The purpose of the conference is to enrich the experience of the customers with personalized recommendations and predictive analysis.
• Registration cost: Free
• Who are the major sponsors: Pandera

8. PerkinElmer Atlanta Automation and Mass Spectrometry Seminar, Atlanta

• About the conference: The conference will have guest speakers discussing applications of mass spectrometry and instrumentation.
• Event Date: June 11, 2019
• Venue: PerkinElmer Tech Center 11695 Johns Creek Parkway #150 Johns Creek, GA 30097 United States
• Days of Program: 1
• Timings: 8:00 AM – 4:00 PM EDT
• Purpose: The purpose of the conference is to focus on the use of Data Science in forensic toxicology, newborn screening analytical methodologies, and disease detection.
• How many speakers: 9
• Speakers & Profile:
  • Sabra Botch-Jones, M.S.M.A Boston University
  • PerkinElmer Team
  • Jamie Foss, Sr. Application Scientist, PerkinElmer
  • Karen Scott, Ph.D., Forensic Science Arcadia University
  • Joseph Jones, Former Director of the Ohio State Patrol Crime Laboratory/J.O. Consulting
  • Collin Hill, M.S., Principal Application Scientist, PerkinElmer
  • Emanuela Gionfriddo, Ph.D., Department of Chemistry and Biochemistry, University of Toledo
  • PerkinElmer
  • Florian Vanderhoven, Director Business Development, Spark Holland
• Registration cost: Free
• Who are the major sponsors: PerkinElmer

1. Southern Data Science Conference, Atlanta

• About the conference: This event was organized by southern data scientists to bring together data scientists from around the country to showcase and discuss their latest research and development in data science.
• Event Date: 7 April, 2017
• Venue: Hyatt Regency Atlanta Perimeter, 4000 Summit Blvd NE · Atlanta, GA
• Days of Program: 1
• Timings: 7 A.M. to 6 P.M.
• Purpose: The purpose of the conference was to provide a platform for experts in data science to demonstrate their innovations in the areas of data science.

2. MLconf Atlanta: The Machine Learning Conference, Atlanta

• About the conference: The conference helped the attendees in learning the latest trends in machine learning.
• Event Date: 15 September, 2017
• Venue: The Academy of Medicine
• Purpose: The purpose of the conference was to discuss the latest research in machine learning techniques and practices, application of tools, algorithms, and platforms to solve the issues pertaining to data sets.
• How many speakers:18
• Speakers & Profile:
  • Alexandra Johnson - Software Engineer, SigOpt
  • Anna Kiefer - Software Engineer, Kevala
  • Aran Khanna - Software Engineer, Amazon Web Services
  • Ashrith Barthur - Security Data Scientist, H20.ai
  • Greg Werner - Co-Founder / CEO, 3blades
  • Jennifer Marsman - Principal Developer Evangelist, Microsoft
• Who were the major sponsors:
  • 3blades
  • Airbnb
  • Amazon
  • CRC Press
  • H2O.ai
  • HiringSolve

3. Big Data Technology Workshop and 2017 HPCC Systems Summit Community Day, Atlanta

• About the conference: The Conference brought together data scientists, professionals, and engineers to discuss the use of HPCC Systems Platform and ECL, to solve issues related to data analytics.
• Event Date: October 3-4, 2017
• Venue: Ritz Carlton Buckhead, 3434 Peachtree Rd NE, Atlanta, GA 30326, USA
• Days of Program: 2
• Timings: 9:00 am - 4:00 pm
• Purpose: The purpose of the conference was to develop a better understanding of the HPCC Systems Platform through research projects, and use cases and also to impart knowledge on the use of ECL.
• Registration cost: $100 for each day

4. 7th Global Tech Mining Conference, Atlanta

• About the conference: The conference showcased presentations on development of new methods, enhanced software tools, and case studies in the field of Big Data.
• Event Date: October 10-12, 2017
• Venue: Georgia Tech Global Learning Center 84 Fifth Street N.W. Atlanta, GA 30308-1031
• Days of Program: 3
• Purpose: The purpose of this conference was to connect researchers, analysts, managers, policymakers, and software specialists to enhance the use of textual information in various fields, and discuss the challenges faced in Text-mining tools and methods, Data, and Applied research.

5. DataSciCon: Data Science, Data Analytics, Machine Learning, and Big Data conference, Atlanta

• About the conference: The conference conducted workshops on Data Science with R Workshop, Data Analytics with Tableau, and Introduction to Machine Learning with Python and TensorFlow followed by sessions to develop a better understanding of Machine Learning, Data Visualisation, Artificial Intelligence, Deep Learning, Data Science, and Big Data.
• Event Date: November 29, 2017, to December 1, 2017
• Venue: Georgia Tech Global Learning Center, 84 5th St NW, Atlanta
• Days of Program: 3
• Purpose: The purpose of this conference was to bring together data analysts, data scientists, and data engineers and impart knowledge on technical aspects of Machine Learning, Data Science, Artificial intelligence, Data Analytics, Big Data, and Deep Learning and their applications.

6. IIA 2018 Analytics Symposium, Atlanta

• About the conference: This Conference brought together innovative speakers to discuss the latest and upcoming trends and analytics and the challenges faced. 
• Event Date: 10 October, 2018
• Purpose: The purpose of this conference was to bring together IIA’s clients, advisory network members, and analytics experts to share their opinions and ideas related to the latest and upcoming trends in the analytics industry.
• Speakers & Profile:
  • Paul Ballew
  • Abhi Seth
  • Alex Barclay
  • Tom Davenport and Kathy Koontz
  • Sameer Chopra
  • Jennifer Priestley
  • Mano Mannoochahr
• Who are the major sponsors:
  • Workiva
  • PWC
  • EY
  • KPMG
  • RSM
  • Protiviti
  • RGP

7. Southern Data Science Conference, Atlanta

• About the conference: This event was organized by southern data scientists to bring together data scientists from around the country to showcase and discuss their latest research and development in data science.
• Event Date: 13-14 April, 2018
• Venue: Atlanta Marriott Buckhead Hotel & Conference Center 3405 Lenox Rd NE, Atlanta, GA 30326· Atlanta, GA
• Days of Program: 2 
• Purpose: The purpose of the conference was to provide a platform for experts in data science to demonstrate their innovations in the areas of data science.

Below are the steps to get a data science job-

• Getting started
• Mathematics
• Libraries
• Data visualization
• Data preprocessing
• Machine Learning and Deep Learning
• Natural Language processing
• Polishing skills

1. Getting started: Choose a programming language that you are proficient and comfortable working in. R and Python are two very popular open-source programming languages for data analysis. Also, read through all the roles and responsibilities of a data scientist.
2. Mathematics: You need to have a good command over mathematics and statistics to make complex data more accessible, understandable and usable. Here are a few topics that you must have a good grasp on:
• Inferential statistics
• Probability
• Descriptive statistics
• Linear algebra
3. Libraries: Libraries are an important part of data science. They help in creating graphs and charts for visualizing the data, applying machine learning algorithms, plotting of processed data, and data preprocessing, etc. Some of the famous libraries include Pandas, Scikit-learn, Ggplot2, SciPy, NumPy and Matplotlib.
4. Data visualization: It is the job of a data scientist to find patterns in the data and make it simple for the non-technical members of the team. For this, data visualization is used. Matplotlib - Python Ggplot2 - R. 
5. Data preprocessing: Most of the data that is generated today has no pre-defined format or organization. So, to make this data ready for analysis, data scientists need to preprocess this data. It is performed using feature engineering and variable selection. Once this is done, ML tools are used for analyzing the data.
6. ML and Deep learning: Machine learning and deep learning skills are a must for analyzing data. Deep learning is preferred while dealing with a huge set of data. You should have a thorough knowledge of topics like RNN, CNN, Neural Networks, etc.
7. Natural Language processing: Expertise in Natural Language Processing is essential for every data scientist. This includes processing and classification of text form of data.
8. Polishing skills: You can participate in competitions like Kaggle etc. to polish and exhibit your data science skills. Another way to do this is to take on real-world projects. You can try exploring new solutions to already solved problems as well.

Follow the below steps to increase your chances of success for the job of Data Scientist-

• Study: To qualify for an interview, learn all quintessential topics, including-
• Probability
• Statistics
• Statistical models
• Machine Learning
• Understanding neural networks
• Meetups and conferences: Begin developing your system or expanding your connections by visiting Tech meetups and data science gatherings. 
• Contests: Grasp, test and keep refining your aptitudes by looking into online competitions like Kaggle.

The responsibility of a data scientist is to analyze the vast amount of structured and unstructured data, look for patterns, and inference information. This is done in order to meet the needs and goals of the business.  

Today, tons of data is generated every day and this has increased the importance of a Data Scientist. This is because the generated data is filled with ideas and patterns that can help in the advancement of the business. It is the job of a Data Scientist to study the data, extract the relevant information and make sense of this data so that it can benefit the business. 

Data Scientist Roles & Responsibilities:

• First, the data, relevant to the business, is fetched. This data can be structured as well as unstructured. 
• Next comes the organization and analysis of the data. 
• After this, programs, tools, and Machine Learning techniques are created to make sense of the data.
• Lastly, statistical analysis is performed on this data to predict future outcomes.

The average salary for a Data Scientist is $101,323 per year in Atlanta, GA.

The Data Science career path can be explained in the following way:

• Business Intelligence Analyst: A Business Intelligence Analyst’s job is to figure out the latest trends of the business and the market. This can be done by analyzing the data to get a clear picture of where their business stands in the market. 
• Data Mining Engineer: The job of a Data Mining Engineer is to examine the needs of the business by studying the data. They also do the job as a third party. Apart from this, a Data Mining Engineer is also responsible for aiding in the data analysis by creating a sophisticated algorithm. 
• Data Architect: A Data Architect is responsible for working alongside system developers, designers, and users for creating blueprints. These blueprints are then used by the data management system for integrating, maintaining, centralizing and protecting the data sources. 
• Data Scientist: A Data Scientist’s job is the pursuit of a business case after analyzing the data, developing hypotheses and an understanding of data required for exploring patterns. Next, they also create the system and the algorithm for the productive use of the data. This furthers the interests of the business. 
• Senior Data Scientist: The role of a Senior Data Scientist is anticipating the needs of the business in the future. They are also responsible for shaping the system, data analysis and the projects to suit the needs of the business. 

• Atlanta Big Data User Group
• Big Data Developers in Atlanta
• Data Science ATL
• Data Science & Business Intelligence Society of Atlanta
• Startup Atlanta Meetups

The best way to obtain a job in Atlanta, GA is through Referrals. Some of the additional ways to network with data scientists are:

• Data science conference
• An online platform like LinkedIn
• Social gatherings like Meetup 

We have compiled the key points, which the employers generally look for while hiring data scientists:

• Education: A Bachelor’s degree is a must in Computer Science or related fields. Many institutes are also introducing special courses on data science. Having a Master’s or a Ph.D. in Data Science will boost up your profile and will help you get recognition among various candidates. 
• Programming: There are no prerequisites for learning a programming language like Python, aside from basic computer skills. Python is a great programming language for data scientists. It is the preferred choice for data scientists. Apart from that SQL is a must no matter what you specialize in..
• Machine Learning: Machine Learning is a fast emerging technology. It will be beneficial for you to gain some hands-on experiences on this. It is recommended to have good knowledge of various Supervised and Unsupervised learning algorithms such as:
  • Random Forest
  • Clustering (for example K-means)
  • Logistic Regression
  • K Nearest Neighbor
  • Linear Regression.
• Projects: Many projects are available to take up online. You can refine your search by choosing your level of difficulty, starting from beginner to proficient, based on your knowledge and confidence with the tools and technology. Such projects will boost your profile and help you get closer to your desired job.

• Being a multi-paradigm programming language makes Python one of the most common and popular languages used by data scientists. It has multiple facets that help the data scientists in their projects. This structured, object-oriented programming language has several libraries and packages useful for data science purposes.
• Python is simple and readable. This makes it the most preferred programming language used by the data scientists. It comes with customized packages and libraries perfectly suitable for the field of data science.
• If you ever get stuck in a python code or while building a data science model using python, there is a broad and diverse range of resources that can help you get out of it. All these resources are available at the disposal of a data scientist.
• Using python comes with a big advantage, the support of the vast python community. Python is a popular language used by millions of developers worldwide. So, if you get stuck somewhere, there is a huge chance that someone has been stuck there before and found a solution for it. And if your problem is new, the helpful python community will try to find a solution for you.

Here are the 5 most popular programming language used in the field of Data Science:

• R: Despite the steep learning curve, R language has the following advantages:
  • R comes along with high-quality open source packages created by the open source community. 
  • It is capable of handling matrix operations and statistical functions. 
  • With the help of ggplot2, R acts as a great data visualization tool.
• Python: It is one of the most popular and sought after languages used in the field of Data Science. Even though it has fewer packages than R, it offers the following advantages that make up for it:
  • Most of the libraries needed in Data Science are provided by Pandas, scikit-learn, and tensorflow 
  • It is very easy to learn, understand, and implement. 
  • It also comes with a big open-source community.
• SQL: SQL stands for structured query language. It works on relational databases.
  • The syntax of SQL is easy to learn and understand.
  • Updating, querying, and manipulating data is very efficient using SQL.
• Java: Despite the verbosity limit of Java and the less number of libraries that can be used for Data Science, the language offers the following advantages:
  • Compatibility. It is very easy to integrate java in data science projects as there are systems pre-coded in Java. 
  • Overall, Java is a general purpose, compiled and high-performance language. 
• Scala: It is one of the most preferred languages in Data Science despite its complex syntax and running on JVM. It is because of the following reasons:
  • Scala program can run on Java too as it runs on JVM as well. 
  • High-performance cluster computing can be achieved by using Scala with Apache Spark. 

If you want to download and install Python 3 on Windows, you need to follow these steps:

• Download and setup: First, visit the download page and through the GUI installer, start installing python on the windows. While installing, make sure that select the checkbox that asks you to add Python3.x to PATH, the classpath. This will allow using the functionalities of python directly from the terminal.

Alternatively, you can use Anaconda to install python 

If you want to check if Python is installed in the system, you can try using the following command that displays the version of the language installed on the system:

python --version

• Update and install setuptools and pip: Use the following command for installing and updating 2 of most crucial libraries (3rd party):

python -m pip install -U pip

To install python 3 on Mac OS X, just follow the below steps:

1. You should install GCC first which can be obtained by downloading Xcode, the smaller Command Line Tools (must have an Apple account) or the even smaller OSX-GCC-Installer packageInstall.
2. While OS X comes with a large number of Unix utilities, a package manager is a key component. 
3. To install Homebrew, open Terminal or your favorite OS X terminal emulator and run

$ ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

4. Insert the Homebrew directory at the top of your PATH environment variable. For this, add the following line at the bottom of your ~/.profile file

export PATH="/usr/local/opt/python/libexec/bin:$PATH"

5. You can install Python 3 by writing the following code

$ brew install python