Data Science Certification Bootcamp in Sprint IT Solutions
The Data Science Training at Spring IT Solutions is a comprehensive program designed to equip individuals with the knowledge and skills required to excel in the field of data science.
Collaborative Learning and Career Building
At the end of most Data Science Certification Bootcamp lessons, you'll have access to an online discussion. Engaging actively and constructively in these discussions can significantly boost your Data Science career development. By offering help or seeking assistance from the trainers, you’ll build meaningful relationships and create valuable professional connections.
These discussions are more than just a conversation to share ideas—they're designed to accelerate your Data Science learning journey. That's why it is made an essential part of our courses: to support your growth and help you enhance your Data Science skills through collaboration and shared insights.
Advantages of enrolling up for Data Science Certification Bootcamp

- Online Training
- Classroom Training
- Career Guidance
Details to know about Data Science Certification Bootcamp
Who Can Take This Course
Our data science course in the USA is designed to be flexible for all skill levels—beginners, intermediates, and advanced learners.
This course suits beginners and experts, covers essential data analysis skills, and offers advanced topics for experienced learners across a wide range of data science topics, from statistics to machine learning.
- Beginners: Those with no prior experience can start with foundational concepts and gradually build their skills.
- Intermediate Learners: Individuals with some knowledge can deepen their understanding and tackle more complex topics.
- Advanced Students: Experienced data scientists can refine their skills and explore specialized areas, ensuring continuous career growth.
Trainers
Sprint IT Solutions's mentors are seasoned professionals from leading tech companies who bring real-world insights and practical experience to the table. They are Industry-leading experts who provide Career-focused learning and dedicated One-on-One Support.
Our Renowned faculty from top universities hasdecades of experience in applied AI, machine learning, and deep learning applications. They provide deep theoretical knowledge, research-driven education, and insights into cutting-edge research.
Our individualized career coaching and personalized learning paths ensure students receive tailored advice and support.
Institution:
Sprint IT Solutions offers the best personalized learning experience with top industry-leading mentors and a cutting-edge curriculum designed to transform careers in data science.
Duration:
- Duration:6-9 months (Part-time and full time)
- Format:Online and in-person Training
- Our data science course includes 100 hours of comprehensive training
Flexible Payment Options for Our Data Science Course
Tuition Fees: $800 - $1500
Payment plan:
- Full Payment
- Partial Payment
- Installment Plan
Location--- 7905 Apt c, Arrowhead cr, Fairfax, VA – 22031
Career opportunities:
Here are the career opportunities after completing our data science course in the USA:
- Data Scientist
- Data Analyst
- Data Engineer
- Data Architect
- Machine Learning Engineer
- Business Intelligence Analyst
Online and Offline
Online Training
- Interactor-led Online Training
- Hands-on projects and exercises
- Discussion forums and Q&A sessions
- Access to a vast library of resources and materials
Offline Training
- Immersive workshops and seminars
- Collaborative group projects and case studies
- One-on-one mentorship and guidance from industry experts
- Networking opportunities with fellow data science enthusiasts
Data Science Certification Bootcamp
Introduction
Our Data Science Certification Bootcamp is an intensive program designed to equip you with the skills and knowledge required to excel in the field of data science. This bootcamp is tailored for those who want to gain a comprehensive understanding of data science concepts and apply them in real-world scenarios.
Demand of Data science job in USA
According to the U.S. Bureau of Labor Statistics, data scientist positions will continue to be among the fastest-growing jobs in 2024. The projected increase in job openings from 2022 to 2032 is 35%.
In addition, the World Economic Forum’s Future of Jobs 2023 report estimates that by 2027, the demand for AI and machine learning specialists will increase by 40%, and for data analysts, scientists, engineers, BI analysts, and other big data and database professionals will grow by 30%–35%.
Data science tools you will learn
Here are 9 crucial data science tools that covered in our comprehensive data science training program:
Python
R
SQL
TensorFlow
Power BI
Tableau
Jupyter Notebook
Microsoft Excel
MATLAB
Who can take up this "Data Science Certification Bootcamp" certification course?
Beginners
Individuals with little to no prior knowledge of data science, programming, or statistics, can join this "Data Science Certification Bootcamp" certification course
Intermediate
Those who have some experience with data analysis or programming but want to deepen their understanding of data science concepts and tools. This level will also enhance their existing skills and introduce new tools and methodologies.
Professionals
Individuals already working in related fields (like data analysis, software development, or business intelligence) looking to transition into data science or enhance their current skill set or those looking for promotion.
Which city is best for data science jobs in USA?
Some of the demanding Cities for Data Scientist in USA.
San Francisco,
CA San Jose, CA
Oakland, CA
Fremont, CA
Chandler, AZ
Pittsburgh, PA
Los Angeles, CA
Among these, Silicon Valley, CA area is home to some of the most famous technology companies in the world, including Meta (home of Facebook and Instagram), Apple, and Google. There are also research-focused data science jobs in the area through Stanford University and NASA's Ames Research Center.
Which state has best opportunities for Data Scientist in USA?
There are various states in USA offers wider data science career opportunities, one among them is California.
The state boasts an abundance of tech giants, start-ups, and innovative companies, offering a plethora of opportunities for those skilled in data science. With a high demand for talent and competitive salaries, California remains a top choice for data scientists looking to advance their careers.
Course curriculum
Module 1: Foundations of Data Science
Description:Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. In this first module we will introduce to the field of Data Science and how it relates to other fields of data like Artificial Intelligence, Machine Learning and Deep Learning.
Introduction to Data Science
High level view of Data Science, Artificial Intelligence & Machine Learning
Subtle differences between Data Science, Machine Learning & Artificial Intelligence
Approaches to Machine Learning
Terms & Terminologies of Data Science
Understanding an end to end Data Science Pipeline, Implementation cycle
Module 2: Math for Data Science, Machine Learning and Artificial Intelligence Description: Mathematicsis veryimportantin the field ofdata scienceas concepts within
mathematicsaid in identifying patterns and assist in creating algorithms. The understanding of various notions of Statistics and Probability Theory are key for the implementation of such algorithms indata science.
Linear Algebra
Matrices, Matrix Operations
Eigen Values, Eigen Vectors
Scalar, Vector and Tensors
Prior and Posterior Probability
Conditional Probability
Calculus
Differentiation, Gradient and Cost Functions
Graph Theory
Module 3: Statistics for Data Science
Description:This module focuses on understanding statistical concepts required for Data Science, Machine Learning and Deep Learning. In this module, you will be introduced to the estimation of various statistical measures of a data set, simulating random distributions, performing hypothesis testing, and building statistical models.
Descriptive Statistics
Types of Data (Discrete vs Continuous)
Types of Data (Nominal, Ordinal)
Measures of Central Tendency (Mean, Median, Mode)
Measures of Dispersion (Variance, Standard Deviation)
Range, Quartiles, Inter Quartile Ranges
Measures of Shape (Skewness and Kurtosis)
Tests for Association (Correlation and Regression)
Random Variables
Probability Distributions
Standard Normal Distribution
Probability Distribution Function
Probability Mass Function
Cumulative Distribution Function
Inferential Statistics
Statistical sampling & Inference
Hypothesis Testing
Null and Alternate Hypothesis
Margin of Error
Type I and Type II errors
One Sided Hypothesis Test, Two-Sided Hypothesis Test
Tests of Inference: Chi-Square, T-test, Analysis of Variance
t-value and p-value
Confidence Intervals
Module 4: Python for Data Science Python for Data Science
Numpy
Pandas
Matplotlib & Seaborn
Jupyter Notebook
Numpy
NumPy is a Python library that works with arrays when performing scientific computing with Python. Explore how to initialize and load data into arrays and learn about basic array manipulation operations using NumPy.
Loading data with Numpy
Comparing Numpy with Traditional Lists
Numpy Data Types
Indexing and Slicing
Copies and Views
Numerical Operations with Numpy
Matrix Operations on Numpy Arrays
Aggregations functions
Shape Manipulations
Broadcasting
Statistical operations using Numpy
Resize, Reshape, Ravel
Image Processing with Numpy
Pandas
Pandas is a Python library that provides utilities to deal with structured data stored in the form of rows and columns. Discover how to work with series and tabular data, including initialization, population, and manipulation of Pandas Series and DataFrames.
Basics of Pandas
Loading data with Pandas
Series
Operations on Series
DataFrames and Operations of DataFrames
Selection and Slicing of DataFrames
Descriptive statistics with Pandas
Map, Apply, Iterations on Pandas DataFrame
Working with text data
Multi Index in Pandas
GroupBy Functions
Merging, Joining and Concatenating DataFrames
Visualization using Pandas
Data Visualization using Matplotlib
Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK+
Anatomy of Matplotlib figure
Plotting Line plots with labels and colors
Adding markers to line plots
Histogram plots
Scatter plots
Size, Color and Shape selection in Scatter
Applying Legend to Scatter plots
Displaying multiple plots using subplots
Boxplots, scatter_matrix and Pair plots
Data Visualization using Seaborn
Seaborn is a data visualization library that provides a high-level interface for drawing graphs. These graphs are able to convey a lot of information, while also being visually appealing.
Basic Plotting using Seaborn
Violin Plots
Box Plots
Cat Plots
Facet Grid
Swarm Plot
Pair Plot
Bar Plot
LM Plot
Variations in LM plot using hue, markers, row and column
Module 5: Exploratory Data Analysis
Exploratory Data Analysis helps in identifying the patterns in the data by using basic statistical methods as well as using visualization tools to displays graphs and charts. With EDA we can assess the distribution of the data and conclude various models to be used.
Pipeline ideas
Exploratory Data Analysis
Feature Creation
Evaluation Measures
Data Analytics Cycle ideas
Data Acquisition
Data Preparation
Data cleaning
Data Visualization
Plotting
-
Model Planning & Model Building
-
Data Inputting
Reading and writing data to text files
Reading data from a csv
Reading data from JSON
Data preparation
Selection and Removal of Columns
Transform
Rescale
Standardize
Normalize
Binarize
One hot Encoding
Imputing
Train, Test Splitting
Module 6: Machine Learning
In machine learning, computers apply statistical learning techniques to automatically identify patterns in data. This module on Machine Learning is a deep dive to Supervised, Unsupervised learning and Gaussian / Naive-Bayes methods. Also you will be exposed to different classification, clustering and regression methods.
Introduction to Machine Learning
Applications of Machine Learning
Supervised Machine Learning o Classification
o Regression
Unsupervised Machine Learning
Reinforcement Learning
Latest advances in Machine Learning
Model Representation
Model Evaluation
Hyper Parameter tuning of Machine Learning
Evaluation of ML
Estimating and Prediction of Machine Learning Models
Deployment strategy of ML
Module 7: Supervised Machine Learning – Classification
Supervised learning is one of the most popular techniques in machine learning. In this module, you will learn about more complicated supervised learning models and how to use them to solve problems.
Classification methods & respective evaluation
K Nearest Neighbors
Decision Trees
Naive Bayes
Stochastic Gradient Descent
SVM –
Linear
Non linear
Radial Basis Function
-
Random Forest
-
Gradient Boosting Machines
-
XGboost
-
Logistic regression
-
Ensemble methods
Combining models
Bagging
Boosting
Voting
Choosing best classification method
Model Tuning
Train Test Splitting
K-fold cross validation
Variance bias tradeoff
L1 and L2 norm
Overfit, underfit along with learning curves variance bias sensibility using graphs
Hyper Parameter Tuning using Grid Search CV
Respective Performance measures
Different Errors (MAE, MSE, RMSE)
Accuracy, Confusion Matrix, Precision, Recall
Module 8: Supervised Machine Learning - Regression
Regression is a type of predictive modelling technique which is heavily used to derive the relationship between variables (the dependent and independent variables). This technique finds its usage mostly in forecasting, time series modelling and finding the causal effect relationship between the variables. The module discusses in detail about regression and types of regression and its usage & applicability
Regression
Linear Regression
Variants of Regression o Lasso
o Ridge
Multi Linear Regression
Logistic Regression (effectively, classification only)
Regression Model Improvement
Polynomial Regression
Random Forest Regression
Support Vector Regression
Respective Performance measures
Different Errors (MAE, MSE, RMSE)
Mean Absolute Error
Mean Square Error
Root Mean Square Error
Module 9: Unsupervised Machine Learning
Unsupervised learning can provide powerful insights on data without the need to annotate examples. In this module, you will learn several different techniques in unsupervised machine learning.
Clustering
K means
Hierarchical Clustering
DBSCAN
Association Rule Mining
Association Rule
Market Basket Analysis using Apriori Algorithm
Dimensionality reduction using Principal Component analysis (PCA)
Module 10: Natural Language Processing
Natural language is essential to human communication, which makes the ability to process it an important one for computers. In this module, you will be introduced to natural language processing and some of the basic tasks.
Text Analytics
Stemming, Lemmatization and Stop word
POS tagging and Named Entity Recognition
Bigrams, Ngrams and colocations
Term Document Matrix
Count Vectorizer
Term Frequency and TF-IDF
Module 11: Advanced Analytics
Advanced Analytics covers various areas like Time series Analysis, ARIMA models, Recommender systems etc.
Time series
Time series
ARIMA example
Recommender Systems
Content Based Recommendation
Collaborative Filtering
Module 12: Reinforcement Learning
Reinforcement learningis an area ofMachine Learningwhich takes suitable action to maximize reward in a particular situation. It is employed by various software and machines to find the best possible behavior or path it should take in a specific situation.
Basic concepts of Reinforcement Learning
Action
Reward
Penalty Mechanism
Feedback loop
Deep Q Learning
Module 13: Artificial Intelligence
Artificial intelligence(AI) is the ability of a computer program or a machine to think and learn. It is also a field of study which tries to make computers "smart"
Artificial Neural Networks
Neural Networks & terminologies
Non linearity problem, illustration
Perceptron learning
Feed Forward Network and Back propagation
Gradient Descent
Mathematics of Artificial Neural Networks
Gradients
Partial derivatives
Linear algebra o Li
LD
Eigen vectors o Projections
-
Vector quantization
-
Overview of tools used in Neural Networks
Tensor Flow
Keras
Module 14: Deep Learning
Deep learning is part of a broader family of machine learning methods based on the layers used in artificial neural networks. In this module, you’ll deep dive in the concepts of Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Auto Encoders and many more.
Deep Learning
Tensorflow & keras installation
More elaborate discussion on cost function
Measuring accuracy of hypothesis function
Role of gradient function in minimizing cost function
Explicit discussion of Bayes models
Hidden Markov Models (HMM)
Optimization basics
Sales Prediction of a Gaming company using Neural Networks
Build an Image similarity
Deep Learning with Convolutional Neural Nets
Architecture of CNN
Types of layers in CNN
Different Filters and Kernals
Building an Image classifier with and without CNN
Recurrent neural nets
Fundamental notions & ideas o Recurrent neurons
Handling variable length sequences
Training a sequence classifier
Training to predict Time series
Module 15: Cloud Computing for Data Science
Cloud computing is massively growing in importance in the IT sector as more and more companies are eschewing traditional IT and moving applications and business processes to the cloud. This section covers detailed information about how to deploy Data Science models on Cloud environments.
Topics
Introduction to Cloud Computing
Amazon Web Services Preliminaries - S3, EC2, RDS
Big data processing on AWS using Elastic Map Reduce (EMR)
Machine Learning using Amazon Sage Maker
Deep Learning on AWS Cloud
Natural Language processing using AWS Lex
Analytics services on AWS Cloud
Data Warehousing on AWS Cloud
Creating Data Pipelines on AWS Cloud
Module 16: DevOps for Data Science
DevOps play a pivotal role in bridging the gap between Development and Operational teams. This section covers key DevOps tools which a Data Scientist need to be aware of for doing their day to day data science work.
Target Audience
The Data Science Certification Bootcamp is perfect for:
- Aspiring data scientists and analysts
- Professionals looking to switch careers to data science
- Students and graduates seeking to enhance their data science skills
- IT professionals aiming to expand their expertise in data analytics
Bootcamp Objectives
By enrolling Data Science Certification Bootcamp, you will:
- Master the fundamentals of data science and analytics.
- Develop proficiency in data science tools and technologies.
- Gain hands-on experience through practical projects and real-world data analysis.
- Prepare for industry-recognized certifications in data science.
Learning Methodology
Our Data Science Certification Bootcamp employs a blend of instructional methods to ensure effective learning:
- Live Online Classes: Interactive sessions with expert instructors.
- Self-Paced Learning: Access to recorded lectures and materials.
- Hands-On Projects: Practical exercises and real-world projects.
- Collaborative Learning: Group discussions and peer feedback.
Instructors
Learn from industry-leading experts with years of experience in data science.
Hands-On Projects
Gain practical experience by working on projects such as:
- Predictive Modeling: Develop models to predict outcomes based on data.
- Data Visualization: Create visualizations to communicate data insights.
- Big Data Processing: Handle and analyze large datasets using Hadoop and Spark.
Certification
Upon completing the Data Science Certification Bootcamp, you will receive a certification that demonstrates your proficiency in data science and analytics. This certification can enhance your resume and LinkedIn profile, showcasing your skills to potential employers.
Prerequisites
No prior experience is required to start this bootcamp. Basic knowledge of mathematics and statistics is helpful but not mandatory.
Enrollment Process
Enrolling in our Data Science Certification Bootcamp is easy:
- Visit Our Website:https://techjobs.sulekha.com/data-science-training
- Complete the Application: Fill out the online registration form.
Career Opportunities
Completing this bootcamp opens doors to various career paths:
- Data Scientist
- Data Analyst
- Machine Learning Engineer
- Business Intelligence Analyst
Support Services
We offer additional support to help you succeed:
- Mentorship: Guidance from experienced data scientists.
- Career Services: Job placement support, resume writing assistance, and interview preparation.
Date & time | Module | Training title | Mode | Training provider / fee | Register |
Data Science | Data Science Certification Bootcamp Provided By Sprint IT Solutions | Data Science institutes |
Online | |||
Data Science | Data Science Certification Bootcamp Provided By Sprint IT Solutions | Data Science institutes |
Online | |||
Data Science | Data Science Certification Bootcamp Provided By Sprint IT Solutions | Data Science institutes |
Online |
Data Science FAQs
What skills or experience should I have before starting to learn data science?
Anyone can start learning data science without prior experience. Basic computer skills and an interest in data are helpful but not required. Those with some knowledge of statistics and coding may skip introductory courses.
How can I determine if pursuing a career in data science is the right choice for me?
Pursuing a career in data science is rewarding for analytical thinkers who enjoy coding and working with data. Data scientists need to learn various programming languages, use data systems and tools for analysis, and solve problems with data. Strong communication skills are crucial for collaborating with teams and sharing insights.
Should I pursue a Data Science course, certification, or degree? How do I determine which option is best for me?
Beginners should consider courses and Professional Certificates, which require less time and money. Start with "What is Data Science?" (part of Data Science Professional Certificate) or a course like "Introduction to Statistics." Both are great for career switchers to acquire the necessary skills for an entry-level job.
What tasks and roles does a data scientist typically handle?
A data scientist performs research to discover patterns in data, manages and examines both structured and unstructured information on a large scale, utilizes technology to forecast intricate data sets, and develops algorithms to gather and analyze data.
What are the essential skills needed to become a data scientist?
Data scientists possess extensive technical expertise in areas such as computer programming, data mining, artificial intelligence, and predictive analytics, enabling them to efficiently organize and analyze data. While technical proficiency is crucial in this field, it is equally important for data scientists to develop strong soft skills, particularly in effective communication.
Is it challenging to learn data science?
With Sprint IT Solutions's resources, courses, and expert instructors, mastering data science becomes straightforward and self-paced. While a background in coding and mathematics can be beneficial, beginners with no prior experience can also quickly grasp the fundamentals through Sprint IT Solutions's introductory courses or boot camps.
What is the typical duration to finish a data science course?
In an introductory course or boot camp, students can grasp the fundamentals of data science within a few weeks. Conversely, comprehensive programs such as undergraduate or master's degrees can span 2-3 years, offering a thorough exploration of core concepts, programming languages, and machine learning.
Is data analytics the same as data science?
Data Analytics and Data Science not exactly same. Data analytics focuses on analyzing datasets to find patterns and insights. Data science encompasses this and further uses programming, math, and statistics to identify trends, solve problems, and create solutions. Many data scientists start as data analysts.
What topics are generally included in data science courses?
Data science courses cover essential topics such as statistics, probability, data visualization, machine learning, data mining, and data wrangling. Advanced courses may include big data technologies, deep learning, and tools like Python, R, and SQL. Practical projects and case studies provide hands-on experience in applying these concepts to real-world problems.
What career paths are available with a data science certificate?
Earning a data science certificate can open numerous career opportunities in various industries, such as technology, finance, healthcare, and marketing. Common roles include data scientist, data analyst, machine learning engineer, and business intelligence analyst. These jobs involve analyzing large datasets, building predictive models, and supporting data-driven decision-making, significantly enhancing your career prospects and advancement potential.
What coding languages are most commonly used in data science?
Python is the most popular programming language for data science, known for its versatility and beginner-friendly nature. R is also popular, especially for statistical analysis. Proficiency in SQL is crucial, as it's used for managing data in relational databases. While Python, R, and SQL are the main languages in data science, languages like Java, C++, JavaScript, and Scala are also used. Familiarity with any programming language can help you quickly learn new ones.