Data Scientist
The Data Science program offers interesting hackathons, masterclasses, webinars, and interactive Ask-Me-Anything sessions. Lock in in viable online instruction to ace Hadoop, Start, R, Python, Machine Learning, and Scene. This program moreover gives openings for live dialogs with industry experts and Machine Learning Engineers, permitting you to develop your Information Science information and collaborate with peers.
Integrate the IBM Advantage into your learningExclusive hackathons, masterclasses and ask-me-anything sessions with IBM.
Immersive Learning Experience8X Higher Live Interaction in Live Data Science Online Classes by Industry Experts
Capstone and over 25+ Industry related Data Science Projects from companies such as Amazon, Walmart, and Mercedes Benz
Annual Average US Salary$86K to $157K
| Title | Description | Date |
|---|
- About Program
- Learning Path
- Reviews
- FAQs
About Data Science Course
This comprehensive Data Science course, developed in collaboration with a leading industry entity, is designed to advance your career and equip you with the training and skills required to succeed in the field of data science. The curriculum provides extensive instruction in key Data Science and Machine Learning concepts, offering hands-on experience with essential tools like Python, R, Tableau, and foundational Machine Learning techniques. The program immerses you in data interpretation, Machine Learning, and powerful programming skills, all aimed at accelerating your Data Science journey.
This IBM-backed Data Science course introduces a collaborative learning approach, integrating online education with industry expertise to prepare you for roles in the data science domain. By joining this course, you gain skills that align with industry demands, validated through research of over 5000 global job descriptions. The program’s high-quality training is recognized as one of the top online offerings in the field.
This Data Science course offers a structured pathway to mastering the skills that define the Data Scientist role, one of the most sought-after professions today. According to the United States Bureau of Labor Statistics, the data science field is set to experience significant growth, with 11.5 million job openings projected by 2026, representing an annual growth rate of 28%. This collaborative program with IBM aims to provide you with essential competencies, including data visualization, regression models, data wrangling, hypothesis testing, data mining, clustering, decision trees, and both supervised and unsupervised learning. The course combines live online sessions with self-paced videos and culminates in a Capstone project, where you apply your knowledge to solve real-world industry problems.
This Data Science course equips you with a diverse skill set crucial for a Data Scientist, including:
In-depth understanding of data organization and manipulation
Proficiency in linear and non-linear regression models and classification methods
Comprehensive knowledge of supervised and unsupervised learning models, including clustering, K-NN, and dimensionality reduction
Advanced mathematical computing with SciPy and NumPy packages
Core concepts of recommendation engines and time series modeling
Mastery in creating interactive dashboards using Tableau
Key Skills Attained Through this Data Science Course Throughout the Data Science course, you will acquire a diverse skill set essential for the Data Scientist role, such as:
• Attaining an intricate comprehension of data organization and manipulation
• Proficiency in employing both linear and non-linear regression models and classification methods for data analysis
• Developing a comprehensive grasp of supervised and unsupervised learning models, encompassing linear regression, logistic regression, clustering, dimensionality reduction, K-NN, and the pipeline process
• Harnessing the capabilities of the SciPy package, including its integral sub-packages like Integrate, Optimize, Statistics, IO, and Weave, for scientific and technical computation
• Cultivating a mastery of mathematical computing with the employment of the NumPy and Scikit-Learn packages
• Assimilating the core principles of recommendation engines and time series modeling, culminating in adeptness in applying these concepts practically through algorithms and real-world applications of machine learning
• Acquiring proficiency in data analysis via Tableau, thereby empowering you to proficiently construct interactive dashboards
The Data Science course offers over 25 industry-relevant projects across various domains, providing practical experience in applying Data Science principles. Here are a few sample projects from the course:
Capstone Project
Engage in mentor-led sessions to create a high-caliber industry project that solves real-world problems. This capstone project focuses on key aspects like data extraction, refinement, visualization, and model development. You have the flexibility to choose a domain-specific dataset, and upon completion, you’ll receive a capstone certificate to showcase your skills to potential employers.
Comcast Telecom Customer Complaints
In this project, you analyze a dataset of customer complaints to improve customer service for Comcast, a leading telecom company. The aim is to identify patterns in customer complaints and recommend solutions to enhance customer satisfaction.
Mercedes-Benz Greener Manufacturing
This project involves optimizing testing durations for Mercedes-Benz vehicles to reduce carbon emissions. By analyzing various feature combinations, you aim to predict the testing time required, contributing to a more efficient manufacturing process.
Retail Analysis with Walmart
This project focuses on predicting sales and demand for Walmart, a major US retailer. You will develop a machine learning algorithm to forecast sales, considering factors like economic conditions, Consumer Price Index (CPI), and unemployment indices.
MovieLens Case Study
Using exploratory data analysis techniques, you examine factors that influence movie ratings. The goal is to understand the features that impact ratings and build a
predictive model to forecast future movie ratings.
These hands-on projects are designed to provide you with real-world experience and strengthen your practical skills, preparing you for a successful career as a Data Scientist.
+1-419-390-4934
( Toll Free )
Data Scientist
- Python for Data Science
- Applied Data Science with Python
- Machine learning
- Why You Choose upskill ?
- Various Options
- What Will You Learn ?
- F.A.Q
Tools Covered
Data Scientist Learning Path
Course 1
Python for Data Science
Start your Python for Data Science journey with this IBM-designed Data Scientist course and get up to speed with programming.
Python for Data Science
Lesson 01: Introduction
Python for Data Science
Course 2
Applied Data Science with Python
Dive deeper into Data Science with Python. This comprehensive course explores essential tools and methods used in Python for data analysis. Learn about data manipulation, mathematical calculations, and other key concepts. Perfect for those aiming for a career in data science.
A. Data Science with Python
B. Lesson 01: Course Introduction
a. 09:05
b. 1.01 Course Introduction
c. 1.02 Demo Jupyter Lab Walk – Through
C. Lesson 02: Introduction to Data Science
a. 2.01 Learning Objectives
b. 2.02 Data Science Methodology
c. 2.03 From Business Understanding to Analytic Approach
d. 2.04 From Requirements to Collection
e. 2.05 From Understanding to Preparation
f. 2.06 From Modeling to Evaluation
g. 2.07 From Deployment to Feedback
h. 2.08 Key Takeaways
D. Lesson 03: Python Libraries for Data Science
a. 3.01 Learning Objectives
b. 3.02 Python Libraries for Data Science
c. 3.03 Import Library into Python Program
d. 3.04 Numpy
e. 3.05 Demo Numpy
f. 3.06 Fundamentals of Numpy
g. 3.07 Numpy Array Shapes and axes Part A
h. 3.08 Numpy Array Shapes and axes Part B
i. 3.09 Arithmetic Operations
j. 3.10 Conditional Statements in Python
k. 3.11 Common Mathematical and Statistical Functions in NumPy
l. 3.12 Indexing and Slicing in Python Part A
m. 3.13 Indexing and Slicing in Python Part B
n. 3.14 Introduction to Pandas
o. 3.15 Introduction to Pandas Series
p. 3.16 Querying a Series
q. 3.17 Pandas Dataframe
r. 3.18 Introduction to Pandas Panel
s. 3.19 Common Functions in Pandas
t. 3.20 Statistical Functions in Pandas
u. 3.21 Date and Time delta
v. 3.22 IO Tools
w. 3.23 Categorical Data
x. 3.24 Working with Text Data
y. 3.25 Iteration
z. 3.26 Plotting with Pandas
aa. 3.27 Matplotlib
bb. 3.28 Demo Matplotlib
cc. 3.29 Data Visualization Libraries in Python Matplotlib
dd. 3.30 Graph Types
ee. 3.31 Using Matplotlib to Plot Graphs
ff. 3.32 Matplotlib for 3d Visualization
gg. 3.33 Using Matplotlib with Other Python Packages
hh. 3.34 Data Visualization Libraries in Python Seaborn An Introduction
ii. 3.35 Seaborn Visualization Features
jj. 3.36 Using Seaborn to Plot Graphs
kk. 3.37 Analysis using seaborn plots
ll. 3.38 Plotting 3D Graphs for Multiple Columns using Seaborn
mm. 3.39 SciPy
nn. 3.40 Demo Scipy
oo. 3.41 Scikit-learn
pp. 3.42 Scikit Models
qq. 3.43 Scikit Datasets
rr. 3.44 Preprocessing Data in Scikit Learn Part 1
ss. 3.45 Preprocessing Data in Scikit Learn Part 2
tt. 3.46 Preprocessing Data in Scikit Learn Part 3
uu. 3.47 Demo Scikit learn
vv. 3.48 Key Takeaways
E. Lesson 04: Statistics
a. 4.01 Learning Objectives
b. 4.02 Introduction to Linear Algebra
c. 4.03 Scalars and vectors
d. 4.04 Dot product of Two Vectors
e. 4.05 Linear Independence of Vectors
f. 4.06 Norm of a Vector
g. 4.07 Matrix
h. 4.08 Matrix Operations
i. 4.09 Transpose of a Matrix
j. 4.10 Rank of a Matrix
k. 4.11 Determinant of a matrix and Identity matrix or operator
l. 4.12 Inverse of a matrix and Eigenvalues and Eigenvectors
m. 4.13 Calculus in Linear Algebra
n. 4.14 Importance of Statistics for Data Science
o. 4.15 Common Statistical Terms
p. 4.16 Types of Statistics
q. 4.17 Data Categorization and types of data
r. 4.18 Levels of Measurement
s. 4.19 Measures of central tendency mean
t. 4.20 Measures of Central Tendency Median
u. 4.21 Measures of Central Tendency Mode
v. 4.22 Measures of Dispersion
w. 4.23 Variance
x. 4.24 Random Variables
y. 4.25 Sets
z. 4.26 Measure of Shape Skewness
aa. 4.27 Measure of Shape Kurtosis
bb. 4.28 Covariance and corelation
cc. 4.29 Basic Statistics with Python Problem Statement
dd. 4.30 Basic Statistics with Python Solution
ee. 4.31 Probability its Importance and Probability Distribution
ff. 4.32 Probability Distribution Binomial Distribution
gg. 4.33 Binomial Distribution using Python
hh. 4.34 Probability Distribution Poisson Distribution
ii. 4.35 Poisson Distribution Using Python
jj. 4.36 Probability Distribution Normal Distribution
kk. 4.37 Probability Distribution Uniform Distribution
ll. 4.38 Probability Distribution Bernoulli Distribution
mm. 4.39 Probability Density Function and Mass Function
nn. 4.40 Cumulative Distribution Function
oo. 4.41 Central Limit Theorem
pp. 4.42 Bayes Theorem
qq. 4.43 Estimation Theory
rr. 4.44 Point Estimate using Python
ss. 4.45 Distribution
tt. 4.46 Kurtosis Skewness and Student’s T- distribution
uu. 4.47 Hypothesis Testing and mechanism
vv. 4.48 Hypothesis Testing Outcomes Type I and II Errors
ww. 4.49 Null Hypothesis and Alternate Hypothesis
xx. 4.50 Confidence Intervals
yy. 4.51 Margin of Errors
zz. 4.52 Confidence Levels
aaa. 4.53 T test and P values Using Python
bbb. 4.54 Z test and P values Using Python
ccc. 4.55 Comparing and Contrastin T test and Z-tests
ddd. 4.56 Chi Squared Distribution
eee. 4.57 Chi Squared Distribution using Python
fff. 4.58 Chi squared Test and Goodness of Fit
ggg. 4.59 ANOVA
i. 4.60 ANOVA Terminologies
hhh. 4.61 Assumptions and Types of ANOVA
iii. 4.62 Partition of Variance
jjj. 4.63 F-distribution
kkk. 4.64 F Distribution using Python
lll. 4.65 F-Test
mmm. 4.66 Advanced Statistics with Python Problem Statement
nnn. 4.67 Advanced Statistics with Python Solution
ooo. 4.68 Key Takeaways
F. Lesson 05: Data Wrangling
a. 5.01 Learning Objectives
b. 5.02 Data Exploration Loading Files Part A
c. 5.03 Data Exploration Loading Files Part B
d. 5.04 Data Exploration Techniques Part A
e. 5.05 Data Exploration Techniques Part B
f. 5.06 Seaborn
g. 5.07 Demo Correlation Analysis
h. 5.08 Data Wrangling
i. 5.09 Missing Values in a Dataset
j. 5.10 Outlier Values in a Dataset
k. 5.11 Demo Outlier and Missing Value Treatment
l. 5.12 Data Manipulation
m. 5.13 Functionalities of Data Object in Python Part A
n. 5.14 Functionalities of Data Object in Python Part B
o. 5.15 Different Types of Joins
p. 5.16 Key Takeaways
G. Lesson 06: Feature Engineering
a. 6.01 Learning Objectives
b. 6.02 Introduction to Feature Engineering
c. 6.03 Encoding of Catogorical Variables
d. 6.04 Label Encoding
e. 6.05 Techniques used for Encoding variables
f. 6.06 Key Takeaways
H. Lesson 07: Exploratory Data Analysis
a. 7.01 Learning Objectives
b. 7.02 Types of Plots
c. 7.03 Plots and Subplots
d. 7.04 Assignment 01 Pairplot Demo
e. 7.05 Assignment 02 Pie Chart Demo
f. 7.06 Key Takeaways
I. Lesson 08: Feature Selection
a. 8.01 Learning Objectives
b. 8.02 Feature Selection
c. 8.03 Regression
d. 8.04 Factor Analysis
e. 8.05 Factor Analysis Process
f. 8.06 Key Takeaways
Course 3
Machine Learning
Secure a prosperous career path through our Machine Learning course. Immerse yourself in this captivating field of Artificial Intelligence through a curriculum that emphasizes practical learning, engaging hands-on labs, four interactive projects, and personalized guidance. Our comprehensive Machine Learning training equips you with the essential expertise necessary for obtaining a Machine Learning certification. Enroll in this online Machine Learning course to cultivate the capabilities essential for excelling as a proficient Machine Learning Engineer in the present day.
A. Lesson 01: Course Introduction
a. 1.01 Course Introduction
b. 1.02 Demo: Jupyter Lab Walk – Through
B. Lesson 02: Introduction to Machine Learning
a. 2.01 Learning Objectives
b. 2.02 Relationship between Artificial Intelligence, Machine Learning, and Data Science: Part A
c. 2.03 Relationship between Artificial Intelligence, Machine Learning, and Data Science: Part B
d. 2.04 Definition and Features of Machine Learning
e. 2.05 Machine Learning Approaches
f. 2.06 Key Takeaways
C. Lesson 03: Supervised Learning Regression and Classification
a. 3.01 Learning Objectives
b. 3.02 Supervised Learning
c. 3.03 Supervised Learning: Real Life Scenario
d. 3.04 Understanding the Algorithm
e. 3.05 Supervised Learning Flow
f. 3.06 Types of Supervised Learning: Part A
g. 3.07 Types of Supervised Learning: Part B
h. 3.08 Types of Classification Algorithms
i. 3.09 Types of Regression Algorithms: Part A
j. 3.10 Regression Use Case
k. 3.11 Accuracy Metrics
l. 3.12 Cost Function
m. 3.13 Evaluating Coefficients
n. 3.14 Demo: Linear Regression
o. 3.15 Challenges in Prediction
p. 3.16 Types of Regression Algorithms: Part B
q. 3.17 Demo: Bigmart
r. 3.18 Logistic Regression: Part A
s. 3.19 Logistic Regression: Part B
t. 3.20 Sigmoid Probability
u. 3.21 Accuracy Matrix
v. 3.22 Demo: Survival of Titanic Passengers
w. 3.23 Overview of Classification
x. 3.24 Classification: A Supervised Learning Algorithm
y. 3.25 Use Cases
z. 3.26 Classification Algorithms
aa. 3.27 Performance Measures: Confusion Matrix
bb. 3.28 Performance Measures: Cost Matrix
cc. 3.29 Naive Bayes Classifier
dd. 3.30 Steps to Calculate Posterior Probability: Part A
ee. 3.31 Steps to Calculate Posterior Probability: Part B
ff. 3.32 Support Vector Machines: Linear Separability
gg. 3.33 Support Vector Machines: Classification Margin
hh. 3.34 Linear SVM: Mathematical Representation
ii. 3.35 Non linear SVMs
jj. 3.36 The Kernel Trick
kk. 3.37 Demo: Voice Classification
ll. 3.38 Key Takeaways
D. Lesson 04: Decision Trees and Random Forest
a. 4.01 Learning Objectives
b. 4.02 Decision Tree: Classifier
c. 4.03 Decision Tree: Examples
d. 4.04 Decision Tree: Formation
e. 4.05 Choosing the Classifier
f. 4.06 Overfitting of Decision Trees
g. 4.07 Random Forest Classifier Bagging and Bootstrapping
h. 4.08 Decision Tree and Random Forest Classifier
i. 4.09 Demo: Horse Survival
j. 4.10 Key Takeaways
E. Lesson 05: Unsupervised Learning
a. 5.01 Learning Objectives
b. 5.02 Overview
c. 5.03 Example and Applications of Unsupervised Learning
d. 5.04 Clustering
e. 5.05 Hierarchical Clustering
f. 5.06 Hierarchical Clustering: Example
g. 5.07 Demo: Clustering Animals
h. 5.08 K-means Clustering
i. 5.09 Optimal Number of Clusters
j. 5.10 Demo: Cluster Based Incentivization
k. 5.11 Key Takeaways
F. Lesson 06: Time Series Modelling
a. 6.01 Learning Objectives
b. 6.02 Overview of Time Series Modeling
c. 6.03 Time Series Pattern Types: Part A
d. 6.04 Time Series Pattern Types: Part B
e. 6.05 White Noise
f. 6.06 Stationarity
g. 6.07 Removal of Non Stationarity
h. 6.08 Demo: Air Passengers I
i. 6.09 Time Series Models: Part A
j. 6.10 Time Series Models: Part B
k. 6.11 Time Series Models: Part C
l. 6.12 Steps in Time Series Forecasting
m. 6.13 Demo: Air Passengers II
n. 6.14 Key Takeaways
G. Lesson 07: Ensemble Learning
a. 7.01 Learning Objectives
b. 7.02 Overview
c. 7.03 Ensemble Learning Methods: Part A
d. 7.04 Ensemble Learning Methods: Part B
e. 7.05 Working of AdaBoost
f. 7.06 AdaBoost Algorithm and Flowchart
g. 7.07 Gradient Boosting
h. 7.08 XGBoost
i. 7.09 XGBoost Parameters: Part A
j. 7.10 XGBoost Parameters: Part B
k. 7.11 Demo: Pima Indians Diabetes
l. 7.12 Model Selection
m. 7.13 Common Splitting Strategies
n. 7.14 Demo: Cross Validation
o. 7.15 Key Takeaways
H. Lesson 08: Recommender Systems
a. 8.01 Learning Objectives
b. 8.02 Introduction
c. 8.03 Purposes of Recommender Systems
d. 8.04 Paradigms of Recommender Systems
e. 8.05 Collaborative Filtering: Part A
f. 8.06 Collaborative Filtering: Part B
g. 8.07 Association Rule: Mining
h. 8.08 Association Rule: Mining Market Basket Analysis
i. 8.09 Association Rule: Generation Apriori Algorithm
j. 8.10 Apriori Algorithm Example: Part A
k. 8.11 Apriori Algorithm Example: Part B
l. 8.12 Apriori Algorithm: Rule Selection
m. 8.13 Demo: User Movie Recommendation Model
n. 8.14 Key Takeaways
I. Lesson 09: Level Up Sessions
a. Session 01
b. Session 02
J. Practice Project
a. California Housing Price Prediction
b. Phishing Detector with LR
Course 4
Tableau Training
Enroll in our Tableau certification program to achieve mastery over Tableau Desktop, an extensively employed tool for data visualization, reporting, and business intelligence across the globe. Elevate your analytics career with our comprehensive Tableau training, acquiring skills that are immediately applicable in professional roles. The Tableau certification holds significant esteem among businesses seeking candidates for data-centric positions, and our online Tableau course is meticulously designed to empower you with the proficiency to adeptly utilize the tool. You’ll learn how to efficiently prepare data, craft interactive dashboards, incorporate diverse dimensions, and delve into exceptional data points through our specialized training.
Tableau Training
Lesson 01: Course Introduction
Lesson 02: Introduction to Data Visualization and Tableau
Lesson 03: Connecting to Various Data Sources and Preparing Data
Lesson 04: Working with Metadata
Lesson 05: Spotlight One
Lesson 06: Filters in Tableau
Lesson 07: Structuring Data in Tableau
Lesson 08: Creating Charts and Graphs
Lesson 09: Spotlight Two
Lesson 10: Calculations in Tableau
Lesson 11: Advanced Visual Analytics
Lesson 12: Dashboards and Stories
Lesson 13: Spotlight Three
Course 5
Data Science Capstone
The Data Science Capstone project presents you with a chance to apply the knowledge acquired during the Data Science course. With the guidance of specialized mentoring sessions, you’ll gain hands-on experience in addressing a genuine Data Science challenge aligned with industry standards. This encompasses tasks spanning data preprocessing, constructing models, and presenting your business findings and insights. Serving as the culminating phase of the Data Science training, this project allows you to showcase your proficiency to potential employers, exhibiting your mastery in the field of Data Science.
- Data Science Capstone
Day 1 – Problem and approach overview
Day 2 – Data pre-processing techniques application on data set
Day 3 – Model Building and fine tuning leveraging various techniques
Day 4 – Dashboard problem statement to meet the business objective
Day 5 – Final evaluation
Data Science Capstone
Master's Program Certificate
Electives
SQL Training
Electives
Data Science with R Programming
Electives
Deep Learning with Keras and TensorFlow
Electives
Industry Master Class – Data Science
Why Online Bootcamp
Develop skills for real career growth
A state-of-the-art curriculum developed in collaboration with industry and education to equip you with the skills you need to succeed in today’s world.
Don’t listen to trainers who aren’t in the game. Learn from the experts who are in the game.
Leading Practitioners who deliver current best practice and case studies in sessions that fit within your workflow.
Learn by working on real-world problems
Capstone projects combining real-world data sets with virtual laboratories for hands-on experience.
Structured guidance ensuring learning never stops
With 24×7 mentorship support and a network of peers who share the same values, you’ll never have to worry about conceptual uncertainty again.
Data Science Training FAQs
Data science is the study of analyzing large amounts of data to extract useful information that helps in making better decisions. Data science is a wide-ranging discipline that covers all aspects of data science. Data science is used by companies that collect large amounts of data. They use various tools and data science techniques to create predictive models. With Simplilearn’s Data Science training, you can learn all the concepts of data science from the ground up.
A Data Science course covers a wide range of topics, whether you are a newbie or an advanced candidate. The duration of a Data Science training program ranges from 6 to 12 months, and is usually conducted by industry professionals to provide candidates with a solid foundation in the subject. In addition to the theoretical content, our course in Data Science includes virtual labs and industry projects, as well as interactive quizzes and practice tests to provide you with an enriched learning experience.
A Data Scientist is responsible for analyzing large data sets to provide actionable insights to business leaders. Data is collected from a variety of sources, formatted for analysis, and then fed into an analytics environment where statistical analysis is conducted to generate actionable insights.
Data scientists analyze large volumes of data, visualize it to discover hidden patterns and trends, and generate actionable insights. These insights help solve complex business issues and make better decisions. Data scientists use data science techniques, such as exploratory data analytics, statistical modeling and machine learning, to discover hidden patterns in data.
Applying to become a Data Scientist is not a walk in the park. To be a Data Scientist, you need to possess several technical skills. The most important technical skills you will need are:
- Mathematics
- Statistics
- Basic Programming
- Machine Learning
- Data Wrangling
- Data Visualization
- Processing Large Data Sets
Data science is being used in nearly every industry, including healthcare, banking and financial services, retail, auto, marketing, manufacturing and government. This Data Science training is useful if you want to work in any of these industries for your career.
