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Data Science with Python Training in Charlotte

Data Science with Python Training in Charlotte
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Why enroll for Data Science with Python course in Charlotte?

pay scale by Edureka coursePython is the preferred language for new technologies such as Data Science and Machine Learning.
IndustriesData Science and Analytics (DSA) job listings is projected to grow by nearly 364,000 listings in 2020 - IBM
Average Salary growth by Edureka courseAccording to the TIOBE index, Python is one of the most popular programming languages in the world.

Data Science with Python Certification Training Benefits in Charlotte

Data science job opportunities are projected to increase by 30% annually, and proficiency in Python programming and data science can unlock vast job prospects. The rise in demand for skilled data scientists and machine learning engineers has prompted more businesses to incorporate machine learning into their operations and help companies analyze and process data, leading to quick and effective decision-making. Obtaining a Data Science and ML Certification from Edureka is the optimal way to secure a lucrative job in this field.
Annual Salary
Data Scientist average salary
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Machine Learning Engineer average salary
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Data Analyst average salary
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Python Developer average salary
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Why Data Science with Python course from edureka in Charlotte

Live Interactive Learning

Live Interactive Learning

  • World-Class Instructors
  • Expert-Led Mentoring Sessions
  • Instant doubt clearing
Lifetime Access

Lifetime Access

  • Course Access Never Expires
  • Free Access to Future Updates
  • Unlimited Access to Course Content
24x7 Support

24x7 Support

  • One-On-One Learning Assistance
  • Help Desk Support
  • Resolve Doubts in Real-time
Hands-On Project Based Learning

Hands-On Project Based Learning

  • Industry-Relevant Projects
  • Course Demo Dataset & Files
  • Quizzes & Assignments
Industry Recognised Certification

Industry Recognised Certification

  • Edureka Training Certificate
  • Graded Performance Certificate
  • Certificate of Completion

About your Data Science with Python course

Data Science with Python Skills Covered in Charlotte

  • skillPython Programming
  • skillStatistical Foundations
  • skillData Analysis and Visualization
  • skillSupervised and Unsupervised Machine Learning
  • skillDatabase Integration with Python
  • skillData Visualization Using Tableau

Data Science with Python Tools Covered in Charlotte

  • Python
  • Jupyter
  • Scikit-Learn
  • NumPy
  • Pandas
  • matplotlib
  • mongoDB

Data Science with Python Course Curriculum in Charlotte

Curriculum Designed by Experts

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Edureka’s Data Science with Python Training in Charlotte will provide hands-on experience in Python programming. We offer live-instructor-led sessions which will help you in mastering the concepts involved in Python. With Edureka’s Data Science Python training in Charlotte, you will learn the essential concepts of Python programming such as Data Structure, Data types, Strings, tuples, lists, basic operators and functions, GUIs, Multi-threading, Data Manipulation, Expressions, Networking, Lambda expressions and much more. Also, in this Data Science Python course, you will gain a thorough knowledge of Python OOPS concepts, modules, Django framework, Database programming, and connectivity to multiple data sources.

Introduction to Data Science and ML using Python

16 Topics

Topics:

  • Overview of Python
  • The Companies using Python
  • Different Applications where Python is Used
  • Discuss Python Scripts on UNIX/Windows
  • Values, Types, Variables
  • Operands and Expressions
  • Conditional Statements
  • Loops
  • Command Line Arguments
  • Writing to the Screen
  • What is Data Science?
  • What does Data Science involve?
  • Era of Data Science
  • Business Intelligence vs Data Science
  • Life cycle of Data Science
  • Tools of Data Science

skillHands-on:

  • Creating “Hello World” code
  • Variables
  • Demonstrating Conditional Statements
  • Demonstrating Loops

skillSkills You will Learn:

  • Basics of Python Programming
  • Command Line Parameters and Flow Control in Python

Data Handling, Sequences and File Operations

12 Topics

Topics:

  • Data Analysis Pipeline
  • What is Data Extraction?
  • Types of Data
  • Raw and Processed Data
  • Data Wrangling
  • Python files I/O Functions
  • Numbers
  • Strings and related operations
  • Tuples and related operations
  • Lists and related operations
  • Dictionaries and related operations
  • Sets and related operations

skillHands-on:

  • Tuple - properties, related operations, compared with the list
  • List - properties, related operations
  • Dictionary - properties, related operations
  • Set - properties, related operations

skillSkills You will Learn:

  • Taking input from the user and performing operations on it
  • Data types in Python

Deep Dive – Functions, OOPs, Modules, Errors, and Exceptions

13 Topics

Topics:

  • Functions
  • Function Parameters
  • Global Variables
  • Variable Scope and Returning Values
  • Lambda Functions
  • Object Oriented Concepts
  • Standard Libraries
  • Modules Used in Python
  • The Import Statements
  • Module Search Path
  • Package Installation Ways
  • Errors and Exception Handling
  • Handling Multiple Exceptions

skillHands-on:

  • Lambda function in Python
  • Errors and Exceptions in Python
  • Packages and Modules in Python
  • Functions - Syntax, Arguments, Keyword Arguments, Return Values
  • Sorting - Sequences, Dictionaries, Limitations of Sorting

skillSkills You will Learn:

  • Object Oriented Concepts
  • Python Functions, Standard Libraries and Modules
  • Handling Exceptions in Python

Introduction to NumPy, Pandas, and Matplotlib

13 Topics

Topics:

  • Data Analysis
  • NumPy - arrays
  • Operations on arrays
  • Indexing, slicing, and iterating
  • Reading and writing arrays on files
  • Pandas - data structures & index operations
  • Reading and Writing data from Excel/CSV formats into Pandas
  • Metadata for imported Datasets
  • Matplotlib library
  • Grids, axes, plots
  • Markers, colors, fonts, and styling
  • Types of plots - bar graphs, pie charts, histograms
  • Contour plots

skillHands-on:

  • NumPy library - Creating NumPy array, operations performed on NumPy array
  • Pandas library - Creating series and data frames, Importing and exporting data
  • Matplotlib library - Using Scatterplot, histogram, bar graph, a pie chart to show information, Styling of Plot

skillSkills You will Learn:

  • Basic Functionalities of the NumPy library in Python
  • Basic Functionalities of the Pandas library in Python
  • Basic Functionalities of the Matplotlib library in Python

Data Manipulation

5 Topics

Topics:

  • Basic Functionalities of a data object
  • Merging of Data objects
  • Concatenation of data objects
  • Types of Joins on data objects
  • Exploring and analyzing datasets
  • Analysing a dataset

skillHands-on:

  • Pandas Function- Ndim(), axes(), values(), head(), tail(), sum(), std(), iteritems(), iterrows(), itertuples(), GroupBy operations, Aggregation, Concatenation, Merging and joining

skillSkills You will Learn:

  • Performing data manipulation using various functionalities of the Pandas library in Python

Introduction to Machine Learning with Python

6 Topics

Topics:

  • What is Machine Learning?
  • Machine Learning Use-Cases
  • Machine Learning Process Flow
  • Machine Learning Categories
  • Linear regression
  • Gradient descent

skillHands-on:

  • Linear Regression – Boston Dataset

skillSkills You will Learn:

  • Machine Learning concepts
  • Machine Learning types
  • Linear Regression Implementation

Supervised Learning - I

6 Topics

Topics:

  • What are Classification and its use cases?
  • What is a Decision Tree?
  • Algorithm for Decision Tree Induction
  • Creating a Perfect Decision Tree
  • Confusion Matrix
  • What is Random Forest?

skillHands-on:

  • Implementation of Logistic Regression, Decision Tree, Random Forest algorithms

skillSkills You will Learn:

  • Supervised Learning concepts
  • Implementing various Supervised Learning algorithms
  • Evaluating model output

Dimensionality Reduction

6 Topics

Topics:

  • Introduction to Dimensionality
  • Why Dimensionality Reduction
  • PCA
  • Factor Analysis
  • Scaling dimensional model
  • LDA

skillHands-on:

  • Implementing PCA
  • Scaling dimensional model
  • Implementing LDA

skillSkills You will Learn:

  • Implementing Dimensionality Reduction Technique

Supervised Learning - II

8 Topics

Topics:

  • What is Naïve Bayes?
  • How Naïve Bayes works?
  • Implementing Naïve Bayes Classifier
  • What is a Support Vector Machine?
  • Illustrate how Support Vector Machine works
  • Hyperparameter Optimization
  • Grid Search vs. Random Search
  • Implementation of Support Vector Machine for Classification

skillHands-on:

  • Implementation of Naïve Bayes, SVM algorithms

skillSkills You will Learn:

  • Supervised Learning concepts
  • Implementing various Supervised Learning algorithms
  • Evaluating model output

Unsupervised Learning

7 Topics

Topics:

  • What is Clustering & its Use Cases?
  • What is K-means Clustering?
  • How does the K-means algorithm works?
  • How to do optimal clustering
  • What is C-means Clustering?
  • What is Hierarchical Clustering?
  • How does Hierarchical Clustering work?

skillHands-on:

  • Implementing K-means Clustering
  • Implementing Hierarchical Clustering

skillSkills You will Learn:

  • Unsupervised Learning concepts
  • Implementation of various Clustering techniques

Association Rules Mining and Recommendation Systems

7 Topics

Topics:

  • What are Association Rules?
  • Association Rule Parameters
  • Calculating Association Rule Parameters
  • Recommendation Engines
  • How do Recommendation Engines work?
  • Collaborative Filtering
  • Content-Based Filtering

skillHands-on:

  • Implementing Apriori Algorithm
  • Performing Market Basket Analysis

skillSkills You will Learn:

  • Data Mining using Python
  • Recommender Systems using Python

Reinforcement Learning (Self-Paced)

9 Topics

Topics:

  • What is Reinforcement Learning?
  • Why Reinforcement Learning?
  • Elements of Reinforcement Learning
  • Exploration vs. Exploitation dilemma
  • Epsilon Greedy Algorithm
  • Markov Decision Process (MDP)
  • Q values and V values
  • Q – Learning
  • Values

skillHands-on:

  • Calculating Reward
  • Discounted Reward
  • Calculating Optimal quantities
  • Implementing Q Learning
  • Setting up an Optimal Action

skillSkills You will Learn:

  • Implementing Reinforcement Learning using Python
  • Developing Q Learning model in Python

Time Series Analysis (Self-Paced)

10 Topics

Topics:

  • What is Time Series Analysis?
  • Importance of TSA
  • Components of TSA
  • White Noise
  • AR model
  • MA model
  • ARMA model
  • ARIMA model
  • Stationarity
  • ACF & PACF

skillHands-on:

  • Checking Stationarity
  • Converting non-stationary data to stationary
  • Implementing Dickey-Fuller Test
  • Plotting ACF and PACF
  • Generating the ARIMA plot

skillSkills You will Learn:

  • TSA Forecasting in Python

Model Selection and Boosting

7 Topics

Topics:

  • What is Model Selection?
  • Need for Model Selection
  • Cross Validation
  • What is Boosting?
  • How do Boosting Algorithms work?
  • Types of Boosting Algorithms
  • Adaptive Boosting

skillHands-on:

  • Performing Cross Validation
  • Implementing AdaBoost using Python

skillSkills You will Learn:

  • Performing Model Selection
  • Boosting algorithms using Python

Statistical Foundations (Self-Paced)

8 Topics

Topics:

  • What is Exploratory Data Analysis?
  • EDA Techniques
  • EDA Classification
  • Univariate Non-graphical EDA
  • Univariate Graphical EDA
  • Multivariate Non-graphical EDA
  • Multivariate Graphical EDA
  • Heat Maps

skillHands-on:

  • Implementing Graphical EDA Techniques
  • Implementing Non-Graphical EDA Techniques

skillSkills You will Learn:

  • Performing EDA on the dataset(s) in Python

Database Integration with Python (Self-Paced)

22 Topics

Topics:

  • Basics of database management
  • Python MySql
  • Create database
  • Create a table
  • Insert into table
  • Select query
  • Where clause
  • OrderBy clause
  • Delete query
  • Drop table
  • Update query
  • Limit clause
  • Join and Self-Join
  • MongoDB (Unstructured)
  • Insert_one query
  • Insert_many query
  • Update_one query
  • Update_many query
  • Create_index query
  • Drop_index query
  • Delete and drop collections
  • Limit query

skillHands-on:

  • CRUD operations using Python MySql and MongoDB

skillSkills You will Learn:

  • Database management systems
  • Database Integration with Python
  • Working of database applications

Data Connection and Visualization in Tableau (Self-Paced)

9 Topics

Topics:

  • Data Visualization
  • Business Intelligence tools
  • VizQL Technology
  • Connect to data from the File
  • Connect to data from the Database
  • Basic Charts
  • Chart Operations
  • Combining Data
  • Calculations

skillHands-on:

  • Connecting to data from File, Database, and Server
  • Performing operations on Hierarchies, Data Granularity and Highlighting feature
  • Creating calculated fields using basic functions
  • Defining LOD expressions
  • Creating Parameters
  • Performing User Input and What-if analysis

skillSkills You will Learn:

  • Data Distribution using various charts in Tableau
  • Combining Data using Joins, Unions and Data Blending
  • Sorting, Filtering and Grouping Techniques
  • Table Calculations in Tableau

Advanced Visualizations (Self-Paced)

10 Topics

Topics:

  • Trend lines
  • Reference lines
  • Forecasting
  • Clustering
  • Geographic Maps
  • Using charts effectively
  • Dashboards
  • Story Points
  • Visual best practices
  • Publish to Tableau Online

skillHands-on:

  • Analyzing data using techniques including Forecasting, Trend Lines, Reference Lines, Clustering, and Geographic Maps
  • Building Dashboard Layout and Formatting
  • Building Story points

skillSkills You will Learn:

  • Advanced visualization techniques in Tableau
  • Building Dashboards and Stories in Tableau

In-Class Project (Self-Paced)

1 Topics

Topics:

  • Predict the species of Plant

skillHands-on:

  • Analyze the data
  • Predict the plant species

skillSkills You will Learn:

  • Data Pre-processing
  • Feature Engineering
  • Implementation of Machine Learning Algorithm

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Data Science Python Training in Charlotte Description

Edureka provides extensive Data Science with Python training in Charlotte to assist you in mastering basics with sophisticated theoretical ideas such as writing scripts, sequence, and file procedures in Python while gaining practical experience with functional apps. This Data Science training is combined with hands-on tasks and live projects. Python is a common high-level, open-source programming language with a broad spectrum of apps for games and web applications in automation, big data, data science, and information analytics development. It's a versatile, powerful, object-oriented and interpreted language that you will learn during this Data Science with Python course in Charlotte.

Why Learn Python for Data Science in Charlotte?

Python has been one of the premier, flexible, and powerful open-source languages that is easy to learn, easy to use, and has powerful libraries for data manipulation and analysis. For over a decade, It has been used in scientific computing and highly quantitative domains such as finance, oil and gas, physics, and signal processing. It's continued to be a favourite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging programs is a breeze in Python with its built in debugger. It has evolved as the most preferred Language for Data Analytics and the increasing search trends also indicate that it is the Next Big Thing and a must for Professionals in the Data Analytics domain.

    What are the objectives of our Data Science with Python Training in Charlotte?

    After completing this Data Science using Python Certification course, you will be able to:
    • Programmatically download and analyze data
    • Learn techniques to deal with different types of data – ordinal, categorical, encoding
    • Learn data visualization
    • Using I python notebooks, master the art of presenting step by step data analysis
    • Gain insight into the 'Roles' played by a Machine Learning Engineer
    • Describe Machine Learning
    • Work with real-time data
    • Learn tools and techniques for predictive modeling
    • Discuss Machine Learning algorithms and their implementation
    • Validate Machine Learning algorithms
    • Perform Text Mining and Sentimental analysis
    • Explain Time Series and its related concepts
    • Gain expertise to handle business in future, living the present

    Edureka offers the best online course for Python Data Science. Enroll now with our data Science with Python training and get a chance to learn from industrial giants.

      Who should go for this Python Data Science online course in Charlotte?

      Edureka’s course is a good fit for the below professionals:
      • Programmers, Developers, Technical Leads, Architects
      • Developers aspiring to be a ‘Machine Learning Engineer'
      • Analytics Managers who are leading a team of analysts
      • Business Analysts who want to understand Machine
      • Learning (ML) Techniques
      • Information Architects who want to gain expertise in
      • Predictive Analytics
      • Professionals who want to design automatic predictive models

      What are the prerequisites for this Data Science with Python Training in Charlotte?

      The pre-requisites for Edureka's Python Data Science course training in Charlotte includes the fundamental understanding of Computer Programming Languages. Fundamentals of Data Analysis practiced over any of the data analysis tools like SAS/R will be a plus. However, you will be provided with complimentary “Python Statistics for Data Science” as a self-paced course once you enroll for the Data Science with Python certification course.

        Data Science with Python Certification Course Projects in Charlotte

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        Riding the digital wave, India's used car market is set to grow at a compounded annual growth of 11% and likely to touch sales of up to 8.3 million units by FY26 as more people h....
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        Industry: Healthcare

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        Data Science with Python Certification in Charlotte

        A skilling ecosystem focused on emerging technologies, powered by a partnership between the Ministry of Electronics and Information Technology, the Government of India, NASSCOM, and the IT industry. It seeks to propel India to become a global hub of talent in emerging technologies. FutureSkills Prime is one of the lighthouse schemes under the Government’s Trillion Dollar Digital Economy initiative.
        You need to complete the modules successfully to earn a Joint Co-Branded Certificate of Completion by NASSCOM FutureSkills Prime and Edureka. On completing the course, the Learner is eligible for Government of India (GOI) incentives after successfully clearing the mandatory NASSCOM Assessment for which the learner will be awarded a NASSCOM certification. For more details please visit: https://futureskillsPrime.in/govt-of-India-incentives.

        • First of its kind government and industry partnership to drive a national skilling ecosystem for digital technologies. 
        • End-to-end skilling from assessment to certification. 
        • Affordable, credible content handpicked by industry leaders. 
        • Speed up learning with bite-sized course modules. 
        • Certifications recognized by the industry
        GoI Incentive can be claimed by all Indian Nationals above 18 years of age. The current programme covers beneficiaries divided into the following broad categories:
        • IT employees in IT Firms and Non-IT firms
        • Non-IT employees aspiring to use new and emerging technologies in their respective domains
        • Employees whose skills for a particular job have become outdated.
        • Central Govt. & State Govt. Employees including employees of PSUs & Autonomous bodies (Govt. Employees)
        • Fresh Recruits who are yet to take up a job, as well as undergoing/selected for internship & Apprenticeship roles in IT/ ITeS

        • Enrolment in the Edureka portal
        • Successful completion of course modules
        • Quizzes, assignments and certificate project submission
        • Assignment and project evaluation by Edureka 
        • Joint co-branded certificate of participation from NASSCOM and Edureka
        • Sign up on the FutureSkills Prime platform for mandatory FutureSkills Prime assessment
        • SSC Certificate issuance by Futureskills Prime on successful completion
        • Avail for GOI incentives upon successful completion of FutureSkills Prime assessment
        To unlock the  Edureka’s Data Science with Python Training course completion certificate, you must ensure the following:
        • Completely participate in this  Edureka’s Data Science with Python Training Course.
        • Evaluation and completion of the quizzes and projects listed.
        Yes, Edureka provides a course completion certificate titled Python for Data Science Professional upon completion of the course and fulfillment of minimum requirements.

        Data Science with Python Certification provides a strong foundation in data science, machine learning, and Python programming. This certification is valuable for several reasons:
        • Demonstrates Mastery of Key Skills: Certification indicates that an individual has a strong understanding of data science concepts, machine learning techniques, and Python programming skills.
        • Improves Job Prospects: Data science and machine learning are high-growth industries, and certification can improve job prospects by demonstrating expertise in these areas.
        • Increases Earning Potential: Certified data scientists and machine learning engineers often earn higher salaries compared to their non-certified peers.
        Please visit the following pages, which will guide you through the top interview questions:
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        Python Data Science Training in Charlotte FAQs

        What is Python for Data Science in Charlotte?

        Data is all around us, and data science will help extract the information. Data science has many applications, and python can be used to implement them. Python is a generic language that can be used to build websites, backend APIs, and scripting. Python's built-in libraries, frameworks, and tools can be used to perform various operations in data science.

        Why should I learn Data Science with Python Course in Charlotte?

        Python is definitely one of the most popular languages in Data Science, which can be used for data analysis, manipulation, and visualization. It has access to many Data Science libraries, making it the perfect language for developing applications and implementing algorithms.

        Where Can I Learn Python for Data Science in Charlotte?

        Although there are many free learning resources available, finding one that teaches data science very well is recommended. You should choose a platform that will teach you interactively and has a curriculum designed to help you along your data science journey. Edureka is one such platform as we offer the best online Data Science with Python course in Charlotte for data science that will take you from beginner to data analyst in Python or data scientist.

        Can I learn a Python for Data Science online in Charlotte?

        Technology has made it easier and more efficient to learn online. It allows you to learn at your own pace without any barriers. Edureka's Data Science with Python certification course offers live classes and online access to study material from any location at any time. You will be able to grasp the key concepts quickly with our extensive and growing collection of tutorials, blogs, and YouTube videos. We offer a 24/7 support service to answer any questions you may have after your class ends.

        Who are the instructors for the Python Data Science Course in Charlotte?

        All the instructors at edureka! are practitioners from the Industry with minimum 10-12 yrs of relevant IT experience. They are subject matter experts and are trained by edureka for providing an awesome learning experience to the participants of Data Science with Python Course in  Charlotte.

        What is the Data Science with Python Course in Charlotte duration?

        Data Science with Python Course in Charlotte can take between five and 10 weeks to learn basic Python programming concepts, including object-oriented programming and basic Python syntax. It is important to note that the time it takes for Python programming depends on your experience with web development, data science, and other related fields.

        What does a Data Science Expert do?

        A Data Science Expert applies statistical, mathematical, and computational techniques to analyze and interpret large and complex datasets to extract insights, make predictions, and inform decision-making. They are skilled in programming languages like Python or R and use various tools and technologies such as machine learning algorithms, data visualization, and database systems to manipulate, process and analyze data. They may work in various industries such as finance, healthcare, marketing, etc.

        Why is it essential to learn Data Science with Python course in Charlotte?

        Python is preferred by data scientists over other languages because it has powerful machine learning libraries that can be used to build any machine learning algorithm. This allows for a better understanding of the current performance without sacrificing existing performance. These powerful frameworks allow data scientists to create the right neural networks. Python is the foundation of Google, YouTube and Instagram. It allows for multiple tasks to be automated and the use of these applications in various languages. The code is simple and well-documented. Many organizations are still not adopting a data-centric approach. The market lacks data literacy. To fill this gap in supply, you will need to study data science and its underlying areas by taking python data science training.

        What type of job can I get after completing a Data Science with Python course in Charlotte?

        This is an industry where opportunities are plenty, so once you have the education and qualifications, the jobs are waiting for you. To name a few, some of the most common job titles for data scientists include:

        • Business Intelligence Analyst
        • Data Mining Engineer
        • Data Architect
        • Data Scientist
        • Senior Data Scientist

        What are the companies hiring Data Scientists in Charlotte?

        There are several companies in Charlotte that hire Data Scientists. Some prominent companies known for their presence in the area and for hiring Data Scientists include:

        • Bank of America

        • Wells Fargo

        • Duke Energy

        • LendingTree

        • Lowe's

        • AvidXchange

        • TIAA

        • Ally Financial

        • Brighthouse Financial

        • Sealed Air

        These are just a few examples, and there are many other companies in Charlotte across various industries such as banking, finance, energy, technology, and more that employ Data Scientists.


        What is the cost of a Data Science with Python Course in Charlotte?

        The cost of a Data Science with Python course is $539.

        What is the salary for a Data Scientist in Charlotte?

        The average salary for a Data Scientist in Charlotte can vary based on factors such as experience, education, industry, and the specific company. However, on average, a Data Scientist in Charlotte can earn around $90,000 to $140,000 per year.

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