Top 5 Data Analytics Courses In Queens With Practical Training
Data analytics courses are the best alternative to college degrees. Students who want to learn effective programs within a short period can undergo and read this content. First of all, if one wants to pursue a data analytics career, they have to search for certification courses. Certification courses are one-year courses that include different topics and concepts related to data analytics. Some students want to pursue full-time postgraduate programs, and in master’s programs, you can find very good universities with reasonable fees. Read the below content to gain knowledge on data analytics and data analytics courses in Queens.
The popularity of data analytics as a career option has increased manifold in the past few years. the reason is that companies have realized that targeted content works best for sustained growth of the business. The impact of data analytics is a global phenomenon and today data analytics course is one of the best training programs that students with a love for numbers and statistics can take up. It offers a wide range of career options and you can explore diverse industries. Now lets look at some of the top data analytics courses in Queens that offer top-notch training with practical assignments.
Best Data Analytics Courses in Queens:
1. IIM SKILLS
IIM SKILLS is the leading no. 1 top listed ed-tech company that offers wide range of courses. Their data analytics course is one of the highly rated and preferred course.
Course Module:
| Module Name | Important Topic |
Module 1: Basic and Advance Excel | Introduction to Data Handling |
| Data Manipulation Using Functions | |
| Data Analysis and Reporting | |
| Data Visualization in Excel | |
| Overview of Dashboards | |
Module 2: Visual Basic Application | Introducing VBA |
| How VBA Works with Excel | |
| Key Components of Programming Language | |
| Programming Constructs in VBA | |
| Functions & Procedures in VBA | |
| Objects & Memory Management in VBA | |
| Error Handling | |
| Controlling Accessibility of Your Code | |
| Communicating with Your Users | |
Module 3: SQL | Basics RDBMS Concepts |
| Utilizing the Object Explorer | |
| Data Based Objects Creation (DDL Commands) | |
| Data Manipulation (DML Commands) | |
| Accessing Data from Multiple Tables Using SELECT | |
| Optimizing Your Work | |
Module 3.1: SQL Server Reporting Services | Basics of SSRS |
| Creating Parameters | |
| Understanding Visualization | |
| Creating Visualization Using SSRS | |
Module 3.2: SQL Server Integration Services | Understanding Basics of SSIS |
| Understanding Packages | |
| Creating Packages to Integrate | |
| Creating Project Using SSIS | |
Module 4: Power BI | Introduction |
| Data Preparation and Modeling | |
| Data Analysis Expressions (DAX) | |
| Reports Development (Visuals in Power BI) | |
Module 5: Data Analytics Using Python | Introduction to Basic Statistics |
| Introduction to Mathematical Foundations | |
| Introduction to Analytics & Data Science | |
| Python Essentials (Core) | |
| Operations with NumPy (Numerical Python) | |
| Overview of Pandas | |
| Cleansing Data with Python | |
| Data Analysis Using Python | |
| Data Visualization with Python | |
| Statistical Methods & Hypothesis Testing | |
Module 6: Tableau | Getting Started |
| Data Handling & Summaries | |
| Reports Development (Visuals in Tableau) | |
Module 7: R For Data Science | Data Importing/Exporting |
| Data Manipulation | |
| Data Analysis | |
| Using R with Databases | |
| Data Visualization with R | |
| Introduction to Statistics | |
| Linear Regression: Solving Regression Problems | |
Module 8: Alteryx | Overview of the Alteryx Course and Fundamental Concepts |
| Using the Select Tool to Rename Fields, Change the Data Type | |
| Understanding the User Environment and Alteryx Settings | |
| Filtering Data/Data Processing | |
| Blending/Joining Data from Different Sources | |
| Data Cleansing | |
| Impute Values | |
| Random Sample |
- Data Analytics Course
- 6 months of live online training session + 2 months of internship
- 472.07 British Pound Sterling + taxes
Tools Covered:
- Python
- R
- Tableau
- SQL
- Excel
- Alteryx
Why Must one choose IIM SKILLS:
- Ranks no. 1
- Best curriculum for the beginners to the experienced professional of any level
- 24*7 career guidance & support
- Best and industry expert mentors
- Lifetime free access to their learning materials.
- Can apply with friends and avail their 10% discount on group registration
- Guaranteed money back after 1st session
- All their courses are online
- Offers flexible learning & also has flexibility is scheduling your session.
- Access to the leaning materials for lifetime
- Financial Modeling Course
- Digital Marketing Course
- SEO Course
- Technical Writing Course
- GST Course
- Content Writing Course
- Business Accounting And Taxation Course
- CAT Coaching
- Investment Banking Course
Contact: +919580740740, [email protected]
2. Data Analytics Courses in Queens – New York (NYC) Data Science Academy
Switchup – 4.89/ 5
Course Report Rating – 5
NYC data science academy provides many courses online. It is a nationally recognized institute. They are providing many boot camps for enhancing a career in data analytics. Here you can have access to a learning management system. The hiring firms are top multinational companies. Due to the pandemic, they started offering online courses.
Data Analytics Courses in Queens- NYC institute is mentioned here. A complete module explanation is provided in this content. NYC institute starts offering courses from basics to advanced techniques. Most of the concepts sound ambiguous, but the itinerary of all the courses will give you complete knowledge.
Introductory Python
● Course Name: Introductory Python
● Course Fee: $1,510.50
● Course Duration: 40-hour program
● Mode of Learning: online
● Prerequisites: No previous knowledge is required
This course is suitable for students who want to excel in data wrangling techniques. This course teaches you different data manipulation techniques.
Course Curriculum:
Module 1: List Manipulation.
● Simple values and expressions.
● Functions and Operations.
● Lists.
● Operators.
● Lists comprehensions.
● Multiple Operations.
Module 2: Strings and Simple I/O Interfaces.
● Characters.
● Strings.
● Operators.
● Input files as a list of strings.
● Print statements.
● Reading data from websites.
Module 3: Control Structures.
● Statements vs Expressions.
● Loops.
● Statements.
Module 4: Data Analysis Packages.
● NumPy.
● Pandas.
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Data Science and Python – Data Analysis and Data Visualization Techniques
● Course Name: Data Science and Python – Data Analysis and Data Visualization Techniques
● Course Fee: $1,510.05
● Course Duration: 40-hour program
● Mode of Learning: online
● Prerequisites: Knowledge of data types and data structures
This course is suitable for students who have prior knowledge of python and data analytics.
Course Curriculum:
Module 1: Introduction to Python.
● Introduction to python notebook.
● Basics objects in python.
● Variables and functions.
● Control flow.
● Data structures.
Module 2: Deep Exploration of Python.
● Introduction to object-oriented programming.
● Dealing with different files.
● Learn to run python scripts.
● Handling and processing of strips.
Module 3: Scientific Computation Tools.
● NumPy.
● SciPy.
Module 4: Data Visualization Techniques.
● Seaborn.
● Matplotlib.
Module 5: Data Manipulation Techniques with Pandas.
● Pandas.
● Final Projects.
Data Science with R Programming – Data Analysis and Visualization Techniques
● Course Name: Data Science with R Programming – Data Analysis and Visualization Techniques
● Course Fee: $2080.50
● Course Duration: 35-hour program
● Mode of Learning: online
● Prerequisites: Basic knowledge of computer components and programming
This course has a comprehensive curriculum that deals with statistical tools, graphs, functions, and more.
Course Curriculum:
Module 1: Basic Programming Techniques with R.
● R programming and its basics.
● R language.
● A detailed study on data objects.
● Functions and programming.
Module 2: Basic Data Elements.
● Data transformation.
● Character and string manipulation.
● Dates and timestamps.
● Web data capturing.
● API (Application Programming Interface) data resources.
● Connection to an external database.
Module 3: Efficient manipulation with dplyr in R programming.
● A subset, transform and reorder.
● Join datasets.
● Operations on various datasets.
Module 4: Data Graphics and Data Visualization Techniques.
● Core ideas on data graphics and data visualization techniques.
● R graphics engines.
● Big data graphics with ggplot2.
Module 5: Advanced Visualization Techniques.
● Customized graphical techniques with ggplot2.
● Other plotting cases in R programming.
Data Science with Python and Machine Learning
● Course Name: Data Science with Python and Machine Learning
● Course Fee: $1890.50
● Course Duration: 20 hours of learning
● Mode of Learning: online
● Prerequisites: Knowledge of Python Programming and ability to wrangle, manipulate, and visualize the data
This course gives you complete knowledge of machine learning and python, and how to apply the python programming language.
Course Curriculum:
Module 1: Introduction to Regression Techniques.
● Machine Learning.
● Linear and Simple Regression.
● NumPy and Scikit.
Module 2: First Classification.
● Logistic Regression.
● Discriminant Analysis.
● Naive Bayes Classifier.
● Supervised Machine Learning.
Module 3: Resampling Techniques and Model Selection.
● Cross-Validation.
● Bootstrap.
● Feature Selection.
● Model Selection and Regularization learning.
Module 4: Second Classification.
● Support Vector Machines.
● Decision Trees.
● Bagging Techniques and Random Forests.
● Decision Trees and Support Vector Machines with Lab.
Module 5: Unsupervised Machine Learning.
● In statistical analysis, Principal Component Analysis (multivariate statistics).
● K means Hierarchical Clustering Techniques.
● Principal Component Analysis and Clustering Lab.
Data Science with R Programming
● Course Name: Data Science with R Programming and Machine Learning
● Course Fee: No Information
● Course Duration: 35 hours of learning
● Mode of Learning: online
● Prerequisites: Knowledge of Python Programming and ability to wrangle, manipulate, and visualize the data
Course Curriculum:
Module 1: Foundations of Statistics and Simple Linear Regression.
● Introduction.
● Statistical Inferences.
● Introduction to Machine Learning.
● Simple Linear Regression.
● Diagnostics and Transformations.
● Coefficient of Determination.
Module 2: Multiple Linear Regression and Generalized Linear Model.
● Multiple Linear Regression.
● Assumptions.
● Extending Model Flexibility.
● Linear Models.
● Logistic Regression.
● Estimations and Interpretations.
● Modular Fit.
Module 3: K-Nearest Neighbors (KNN), Naive Bayes, the Curse of Dimensionality.
● The KNN Algorithm.
● The choice of K and Distant Measure.
● Conditional Probability and Bayer’s theorem.
● The Naive Bayes Algorithm.
● The Laplace Estimator.
● Dimension Reduction.
● PCA Procedure.
● Ridge and Lasso Regression.
● Cross-Validation.
Module 4: Tree Models and Support Vector Machines (SVM).
● Decision Trees.
● Bagging.
● Random Forests.
● Boosting.
● Variables.
● Classifiers.
● Sort Margin and Support Vector Classifier.
● Kernels and SVM.
Module 5: Cluster Analysis and Neural Networks.
● Cluster Analysis.
● K-means Clustering.
● Hierarchical Clustering.
● Neural Networks and Perceptions.
● Propagation and more.
Design and Implementation of Production Machine Learning Systems (MLOps)
● Course Name: Design and Implementation of Production Machine Learning Systems (MLOps)
● Course Fee: $2,840.50
● Course Duration: No Information
● Mode of Learning: online
● Prerequisites: Familiarity with programming language, cloud computing, or software handling experience.
Course Curriculum:
Module 1: Overview of Machine Learning and Production.
● Differentiation between machine learning and practicing in the industry.
● Machine Learning vs Software Engineering.
● Components of Machine Learning.
● Online vs offline machine learning systems.
● Demonstration.
● Hands-on Projects on Google, Projects.
● Hands-on setting up of repository.
Module 2: Machine Learning Engineering and its Fundamentals.
● Software engineering principles.
● System design.
● Machine learning design.
● MLOps concepts and design principles.
● Hands-on experience in machine learning.
● Hands-on experience with Kubernetes.
● Ideating.
Module 3: Feature Systems.
● Introduction to Feature Systems.
● Common Feature Systems.
● Hands-on experience in cloud computing.
● Hands-on experience with feature systems.
● Ideating.
Module 4: Machine Learning Model and Training Pipelines.
● ML training platforms.
● Workflow orchestration and automation.
● Cost and value analysis.
● The setting of the ML pipeline.
● Experience with Kube Flow.
● Designing and Planning.
Module 5: Managing Training Experiments, Machine Learning, and Metadata.
● Models.
● Experimentation on ML Practitioner.
● Hands-on experience with metadata and model registry.
● Hands-on experience in model registries.
● Design and Planning.
Module 6: Deploying Machine Learning Models.
● Offline predictions.
● Online model systems.
● Deployment architecture.
● Hands-on experience in Kube Flow and Dataflow.
● Hands-on experience with ML models.
● Architecture review.
Module 7: Machine Learning Observability.
● Infrastructure and Software Observability.
● Latency, Throughput, availability, and reliability.
● Fairness and bias.
● Hands-on learning on Prometheus and Grafana.
● Learn to access logs and metrics in Google Cloud.
● Logging predictions.
● Architecture review.
Module 8:Experimentation and Reliability Engineering.
● ML Experimentation Design.
● Hands-on experience with Kubernetes.
● Project on Implementation.
Module 9: Continuous Learning of Concepts.
● Differentiate Streaming and Batch Processing.
● Event-driven, asynchronous systems.
● ML systems, incremental model updates.
● Designing and implementing Kubernetes.
● Implementation project.
Module 10: Machine Learning Governance.
● Observability, visibility, and control.
● Monitoring and alerting.
● Model service catalog.
● Security.
● Compliance.
● Presentation.
Natural Language Processing for Production
● Course Name: Natural Language Processing for Production
● Course Fee: $2840.50
● Course Duration: No Information
● Mode of Learning: online
● Prerequisites: Medium level of python knowledge is sufficient to learn this course
NLP is a subsidiary field of artificial intelligence that dive into text analytics, it does not cover object-oriented programming.
Course Curriculum:
● Module 1: Introduction to NLP concepts, Python, and Applications.
● Module 2: Retrieving and Processing of Text Data (Part 1).
● Module 3: Retrieving and Processing of Text Data (Part 2).
● Module 4: How Machines React and Understand the Text (Part 1).
● Module 5: How Machines React and Understand the Text (Part 2).
● Module 6: Supervised Learning in NLP.
● Module 7: Unsupervised learning in NLP.
● Module 8: How to gain insights into text data.
● Module 9: Transfer of Learning Applications.
● Module 10: Semantic Similarity and NLP Productions.
Contact Details:
● Phone: 917-383-2099
● Email: [email protected]
3. Data Analytics Courses in Queens – CareerEra
Irrespective of location, you can learn this master’s program from your comfort zone.
Their content is available online. After registration, you can have full access to study the content.
This course covers all the topics required in data analytics and data science. Earlier NYC institutes had specific courses but this program is different and covers the entire curriculum. It is suitable for undergraduate students who want to establish a sole career in data analytics.
● Course Name: Master’s Program in Data Science
● Course Fee: No Information
● Course Duration: 12 months program
● Mode of Learning: Online
Course Curriculum:
Module 1: Introduction.
● Introduction to Python Programming Language.
● Introduction to R Programming Language.
● DBMS- Database Management System using My SQL.
Module 2: Data Analysis Techniques.
● Statistical Tools and Techniques for Data Science.
● Exploratory Data Analysis.
Module 3: Machine Learning Tools and Techniques.
● Supervised Machine Learning covers Regression Techniques.
● Ensemble Techniques.
● Unsupervised Machine Learning.
● Machine Learning and Model Deployment with the help of Flask.
● Supervised Machine Learning and Classification.
Module 4: Data Visualization Techniques.
● Data Visualization (critical concepts) using Tableau.
● Data Visualization using Power BI.
● Data Visualization using Google Data Studio.
Module 5: Introduction to Artificial Intelligence.
● Time series Analysis in Forecasting.
● Introduction and Explanation to Natural Language Processing.
● Introduction to Neural Networks and Deep Learning Techniques.
● Text Mining and Analytics; Sentiment Analysis.
● Reinforcement Learning Concepts.
● Computer Vision Techniques.
Tools covered in this course:
● Python.
● Tableau.
● SQL (Structured Query Language).
Technologies you learn from the course:
● Regression techniques.
● Predictive modeling.
● Clustering.
● Time series analysis.
● Classification techniques.
● Statistical techniques.
● Data transformation operations.
● Deep Learning.
● Natural Language Processing.
Capstone Projects:
● Retail Project: Concepts used are Market Basket Analysis, RFM (Recency, Frequency, Monitoring) Analysis, Brand Loyalty Analysis, and Time Series Analysis.
● E-commerce: Techniques and concepts used are Text Mining, Analytics, K means, Clustering, Regression Trees, Neural Network, and XG Boost.
● Web and Social Media: Modeling and Clustering Techniques.
● Banking Project: Concepts used are Linear Discriminant Analysis, Logistic Regression, Neural Network, Boosting, Random Forest, and CART (Classification and Regression Tree).
● Supply Chain Project: Same techniques were used in the E-commerce project.
● Health Care Project: Techniques covered areLogistics Regression, Random Tree, Random Forest, and more.
● Insurance Project: Techniques covered NLP (Natural Language Processing), Latent Semantic Analysis, and Vector Space Model.
● Entrepreneurship and Startup Projects: Techniques used are Univariate and Bivariate Analysis, Multinomial Logistics, and Random Forest.
● Finance and Accounts: Concepts and techniques used are Conditional Inference Tree, Logistics Regression, CART, and Random Forest.
Contact Details:
● Phone: +1 844 889 4054
4. Data Analytics Courses in Queens – Transfotech Academy
Transfotech offers three courses on information technology QA engineering, data analytics, and business analytics. Investment, dividend, and financial decisions are the three major decisions in the organization. The business analyst’s role is to acquire data analytics knowledge to take investment decisions in the organizations.
Business Analytics:
● Course Name: Business Analytics
● Course Fee: $4,500
● Course Duration: 6 months: 30 plus classes; 60 plus hours of learning (Beginner level)
● Mode of Learning: Online
Course Curriculum:
● Business Planning and Monitoring.
● Strategy Analysis.
● Business Intelligence.
● Performance Management.
● Decision Making.
● Marketing Performance.
● SQL (Structured Query Language).
● Regression Analysis.
● Data Visualization Techniques.
● Data Modeling.
● Data Cleansing.
● Business Analytics.
Data Analytics:
● Course Name: Data Analytics
● Course Fee: $4,500
● Course Duration: 16 weeks program
● Mode of Learning: Online
Course Curriculum:
● Introduction to Data Analytics.
● Data Mining.
● Visualization Techniques.
● Pattern Identification.
● Python Programming Language.
● Pandas API for Data Indexing.
● Apache Spark Concepts.
● Hadoop.
● Statistics.
● Data Processing Techniques.
Features of the institute:
● Assistance to build your career.
● 360-degree learning: online classes, live lectures, learning management system.
● After completion of the course, a two-week internship on real projects.
● They will guide you to prepare a resume and help you to crack the interviews.
Contact Details:
● Phone: 862 766 3401
● Email:[email protected]
5. Data Analytics Courses in Queens – Career Center
This institute provides 96 plus courses on segregated topics of data analytics. This course is offered as a live online course and through class training in New York. Here in the below content, you can find information on their courses.
Most of the courses fee start from $229 to $549,
Beginner Courses:
● Data Analytics Technologies Boot Camp – 59 hours.
● Microsoft Excel Boot Camp – 21 hours.
● Boot camp on Python for Data Science and Analytics – 30 hours.
● SQL (Structured Query Languages) Boot camp – 24 hours.
● Tableau Boot camp – 14 hours.
● Data Science Certificate Course – 84 hours.
● Beginner Microsoft Excel – 7 hours.
● First level SQL – 8 hours.
● First level Tableau – 7 hours.
● Postgresql Boot camp – 18 hours.
● Data Analytics with R Programming Boot camp – 30 hours.
Courses Not for Beginners:
● Intermediate Level Microsoft Excel – 7 hours.
● Advanced Microsoft Excel – 7 hours.
● Second level SQL – 8 hours.
● Third-level SQL – 8 hours.
● Second level Tableau – 7 hours.
● Excel and Programming with VBA – 16 hours.
● Boot camp on Python and Machine Learning – 30 hours.
Contact Details;
● Phone: (212) 684-5151
● Email: [email protected]
6. Data Analytics Courses in Queens – Brainstation
Google rating – 4.5 out of 5 stars
Course report – 4.5 out of 5 stars
Switchup – 4.5 out of 5 stars
This course is for students who want to start their data analytics career. This course includes different concepts and techniques related to data analytics. It is a New York-based institution, you can learn this course online and also through classroom learning. This course is highly demanding, instructors help you to learn difficult concepts like tableau, and SQL. They will offer you placement support to land a good job.
Course Curriculum:
● Unit 1: Introduction to Data Analytics (Excel- basic to advanced level: Analysis strategy, data collection, and data cleansing).
● Unit 2: Database Operations and Advanced Data Analytics Techniques (exploratory data analysis, SQL, regular expressions, data management, data normalization, and database schema).
● Unit 3: Data Visualization Techniques (data visualization tools and techniques, presentations, dashboards, and communication of the data).
Opportunities in the Data Analytics Field:
● Analytics Consultant.
● Manager in Analytics.
● Data Analyst.
● Data Consultant.
● Data Scientist.
● Data Engineer.
● Machine Learning Engineer.
● Artificial Intelligence Engineer.
● Reporting Analyst.
● Research Executive.
● Statistician.
Also Check,
- Data Analytics Courses in New Zealand
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- Data Analytics Courses in Canada
- Data Analytics Courses In Dubai
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- Data Analytics Courses in Zurich
- Data Analytics Courses in Germany
Frequently Asked Questions of Data Analytics Courses in Queens:
1. How are data analysts different from business analysts?
Data analysts and business analysts’ jobs are the same but they differ in responsibilities. Data analysts collect and analyze the data irrespective of the purpose behind the analysis. Data analysts do not have any decision-making authority. His/her analyzed data have different purposes. On the other hand business, analysts are more related to business decisions. They will consider the company’s past performance and use the analyzed data to perform and compete better and best way in the future.
2. What salary can I expect after data analytics courses in Queens?
With the help of knowledge from data analytics courses in Queens, one can earn,
● $44,000 per year as an entry-level data analyst.
● At medium or median level, one can earn $61,593 per year.
● As one gains experience in the data analytics field, the highest salary will be $86,000 per year.
3. I belong to the non-technical stream, Can I join the data analytics courses in Queens?
You can. Data analytics is a newly emerging technique that does not require any coding techniques. The only requirement is knowledge of analytical and logical thinking, including this advanced mathematics. Now data analytics have become easy. Data analysts just have to learn to handle tools and techniques, and the application of techniques. Void coding techniques make data analytics easy for non-technical students. So, you can join any of the above courses in Queens.
4. Why is data analytics considered a stressful career? What challenges did the data analyst face in the work?
● The concept of data has different terms in different businesses, continuous upgradation is mandatory for data analysts.
● An undergraduate and master’s degree is not enough, continuous lifetime learning is required.
● In addition to analytical and critical thinking skills, data analysts require negotiating skills and techniques to handle the stakeholders like customers, human resources, competitors, suppliers, etc…
● This job demands resourceful work.
● A high level of patience is required, massive time is spent on understanding the complete data, and you have to establish a data pipeline.
● Continuous participation in hackathons, boot camps, workshops, and networking with various people is required.
● Don’t always seek optimal solutions, you learn to work on crazy ideas and crazy solutions.
● Most data analytics courses only teach theoretical concepts, it will not work. You have to choose the institute wisely, which provides a good curriculum and different capstone projects.
5. How many hours of work are required for a data analyst?
In a week, data analysts work for 40 to 60 hours.
Conclusion of Data Analytics Courses in Queens:
Data analytics is important in businesses. They help to integrate the different departments to provide optimized work which is better and less in cost. This analysis helps managers to make better decisions in crucial situations. A keen eye is required for data analysts.
From this content, you have obtained knowledge on data analytics courses, and the pros, and cons, of being a data analyst. The above courses from various institutes provide online data analytics courses which help students to make better decisions for their careers.
These courses are considered the best alternative for full-time degrees. The above-mentioned courses are highly suitable for students and professionals. To know the certain eligibility criteria, check the respective institute website.
