Data Scientist
The Data Science program features exclusive hackathons, masterclasses, webinars, and interactive Ask-Me-Anything sessions. Through online instruction, you’ll gain practical exposure to Hadoop, and Spark ,R, Python, Machine Learning, Tableau, . Enhance your expertise in Data Science and engage in live discussions with fellow professionals and Machine Learning Engineers
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
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About Data Science Course
This comprehensive Data Science course, developed in collaboration with a prominent industry entity, propels your Data Science career forward and equips you with top-tier training and competencies essential for thriving in this realm. The program offers extensive instruction in sought-after Data Science and Machine Learning proficiencies, combined with hands-on experience in utilizing pivotal tools and technologies such as Python, R, Tableau, and foundational Machine Learning concepts. Immerse yourself in data interpretation intricacies, master transformative technologies like Machine Learning, and harness robust programming prowess to elevate your Data Science journey
Collaborative Learning Approach This unique collaboration between educational entities introduces learners to an integrated and balanced method of learning, fostering their expertise in the field of data science. This course, developed in partnership, presents an opportunity for students to attain readiness for prominent positions in the data scientist domain. The excellence of Data Science program is widely acknowledged, being recognized as the leading online educational offering
Learning Objectives and Career Opportunities This Data Science course offers a structured pathway to mastering the skills demanded by one of the most sought-after professions in the present year, Data Scientist. As per the United States Bureau of Labor Statistics, the field of data science is experiencing unparalleled growth, with a projected 11.5 million job openings anticipated by 2026, representing an impressive annual growth rate of 28% collaborative Data Science program with IBM aims to equip you with essential competencies, such as data visualization, regression models, recommendation statistics,linear and logistic regression, data wrangling hypothesis testing, data mining, clustering, decision trees, , engines, as well as supervised and unsupervised learning, among other critical proficiencies. This comprehensive Data Scientist training encompasses a combination of dynamic live online instructor-led sessions and self-paced educational videos co-crafted with IBM. The program culminates with a Capstone project, enhancing your learning by guiding you through the creation of an industry-relevant product that encapsulates the full spectrum of knowledge acquired throughout the training. Ultimately, the expertise gained in this Data Science course will aptly prepare you for a fulfilling career as a proficient Data Scientist.
Data Scientists are esteemed professionals within any organizational hierarchy. According to Glassdoor’s esteemed ranking, Data Scientist claims the third position among the 50 Best Jobs for 2022. The scarcity of skilled Data Scientists and their high demand in the dynamic contemporary job market further underscores their significance. As a Data Scientist, you are entrusted with the task of comprehending business challenges, formulating data analysis strategies, procuring, refining, and structuring requisite data, as well as deploying pertinent algorithms and techniques using appropriate tools, ultimately yielding well-founded recommendations grounded in data-driven insights.
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
Hands-on Projects in the Data Science Course This comprehensive Data Science training incorporates over 25 industry-relevant projects spanning diverse domains, equipping you with a solid grasp of Data Science principles. A selection of the projects you will be engaged in is detailed below:
Capstone Project: Within the ambit of this Data Science course, you will actively participate in mentor-led sessions designed to create a high-caliber industry project. This project-driven endeavor involves tackling pertinent industry challenges, thereby leveraging the proficiencies and technological insights garnered throughout the Data Science online course. The capstone project constitutes a holistic exploration of key facets encompassing data extraction, refinement, and visualization, culminating in the development and optimization of data models. Additionally, you have the freedom to opt for a domain-specific dataset of your choice from the available alternatives. Once the project is successfully completed, you’ll receive a top-notch Data Science capstone certificate—a great way to demonstrate your skills and knowledge to potential employers.
Course Culmination Projects: These projects simulate real-world business scenarios, serving as platforms to practically apply the concepts assimilated during specific course segments. These projects usually take around 3 to 4 hours to complete and are customized as follows:
BUILDING USER-BRANCHED RECORDS FOR AMAZON DOMAIN: E-commerce Utilize a dataset of movie reviews sent in by Amazon customers and conduct data analysis on this Amazon customer movie review repository. The focal point of this endeavor is the construction of a machine learning recommendation algorithm, adept at generating user-specific ratings.”
Sample Projects Covered in the Data Science Course This comprehensive Data Science training encompasses a diverse array of practical projects, allowing you to apply acquired knowledge across various domains. A selection of the projects is elucidated below:
COMCAST TELECOM CUSTOMER COMPLAINTS Domain: Telecommunications In response to suboptimal customer service offered by Comcast—an influential global telecommunication company—this project aims to enhance customer satisfaction. Leveraging an existing repository of customer complaints, the project seeks to effect improvements, rectifying recurrent service shortcomings.
MERCEDES-BENZ GREENER MANUFACTURING Domain: Automobile With a focus on reducing testing durations for Mercedes-Benz vehicles, this initiative tackles the optimization of test bench duration. By analyzing a dataset encompassing diverse feature permutations within Mercedes-Benz cars, the project endeavors to predict the time required for successful testing. This predictive capability has the potential to expedite testing processes, thereby minimizing carbon emissions while maintaining Mercedes-Benz’s stringent standards.
RETAIL ANALYSIS WITH WALMART Domain: Retail Responding to the challenge of forecasting sales and demand accurately, this project centers on Walmart—a prominent US retail establishment. Frequent stock shortages attributed to unforeseen demand fluctuations necessitate the utilization of a machine learning algorithm to address this issue. By crafting a robust machine learning algorithm that factors in economic conditions, Consumer Price Index (CPI), unemployment indices, and other pertinent variables, the project aims to predict demand patterns more precisely.
MOVIE LENS CASE STUDY Domain: Entertainment Leveraging exploratory data analysis techniques, this project engages in an analytical investigation of the factors influencing movie ratings. Through meticulous analysis, the project endeavors to discern key features impacting the ratings of specific movies and subsequently constructs a predictive model to forecast movie ratings.
CUSTOMER SERVICE REQUESTS ANALYSIS Domain: Customer Service Drawing insights from data analysis on New York City’s 311 service request calls, this project emphasizes data wrangling techniques to uncover underlying data patterns. The resultant visualizations facilitate the categorization and prioritization of complaints, thereby enhancing the efficiency of customer service operations.
COMPARATIVE STUDY OF COUNTRIES Domain: Geo-political Undertaking a comparative study of diverse countries, this project leverages sample insurance data sets and world development indicators. By creating a comprehensive dashboard, the project enables the juxtaposition of various parameters across different countries, providing valuable insights into geopolitical and socio-economic dynamics.
SALES PERFORMANCE ANALYSIS Domain: Retail This project involves the creation of an intuitive dashboard to present monthly sales performance within distinct product segments and categories. By enabling clients to identify segments that surpass or fall short of sales targets, the dashboard empowers informed decision-making and strategic planning.
PREDICTING LENDING DEMAND BY REGION DIGITALITY: BANKING FOCUSED ON BANKING
In this project, learners will build a statistical model that predicts loan demand by regional characteristics. Will create web-based dashboard that shows the results of the model and provides stakeholders with the information they need.
Develop a model to predict diabetic patients Domain: Healthcare Coordinated with NIDDK data sets
Diabetes is a serious health issue. The goal of this project is to build a model that can predict diabetic patients using the data set.
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Data Scientist
- Python for Data Science
- Applied Data Science with Python
- Machine learning
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- Various Options
- What Will You Learn ?
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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
This course on Data Science using Python provides a comprehensive introduction to the tools and methods used in Python for analyzing data. Acquiring proficiency in Python is essential for various positions in the field of data science, and this particular course enables you to enhance this skill. By employing a combination of different learning methods, you can grasp Python for data science as well as principles like data manipulation, mathematical calculations, and other related topics. Embark on your journey towards a profession as a data scientist through the training offered in Data Science with Python.
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.
