Data Science Job Oriented Certificate Program

(6 Months)

Elevate your career in Data Science with AppliedTech’s 6-Month Job-Oriented Certification Program. Designed with input from industry experts, this online course provides hands-on training in key tools like Python, SQL, Excel, PowerBI, and OpenAI’s ChatGPT. You’ll gain practical skills essential for today’s tech landscape and apply them during a 2-month certified internship. The program prepares you for real-world challenges and positions you for success in data science roles. Master in-demand techniques and boost your career prospects in just six months.

Enroll in AppliedTech Data Science Training Program: Upgrade Your Skills and Advance Your Career!

In association with

a Singapore based Technology Education Enterprise.

In today’s data-driven world, Data Science has become an essential skill for professionals looking to stay competitive and relevant in their fields. AppliedTech’s Data Science Training Program is expertly designed to equip both beginners and seasoned professionals with the critical skills and knowledge necessary to excel in this high-demand area. Covering a comprehensive range of topics, from foundational concepts in data analysis to advanced machine learning techniques, the program ensures a well-rounded education that prepares you for the challenges of the industry.

Throughout the course, you will gain hands-on experience with industry-standard tools such as Python, SQL, Excel, PowerBI, and OpenAI’s ChatGPT. The flexible online format allows you to learn at your own pace, making it easy to balance your studies with other commitments. The curriculum emphasizes practical application through real-world projects, enabling you to build a robust portfolio that demonstrates your ability to tackle complex data challenges. A standout feature of the program is the 2-month certified internship, providing invaluable experience in a professional setting and enhancing your skills and confidence as a data professional.

Upon completing the program, you will earn a certification that validates your skills, boosting your employability in a competitive job market. Additionally, our career counseling services will help you navigate job opportunities, offering support in resume building, interview preparation, and job placement assistance. With AppliedTech’s Data Science Training Program, you’ll gain not only in-depth knowledge and practical experience but also the support needed to launch or advance your career in Data Science. Join us and take the next step toward a successful future!

An overview of what you will learn from this program.

1. Foundations

– Emerging Technologies

– Basics of Data Science

2. Data Analysis

– Exploring Data Analysis using Excel

– Database Management Systems using MSSQL

– Statistics for Data Science

3. Business Intelligence

– Data Visualization and Storytelling

– PowerBI Reports and Dashboards

4. Programming & Machine Learning using Python

– Basics of Python Programming

– Data Analytics using Python

– Machine Learning Techniques

Program Highlights

Live Projects

Work on hands-on data science projects, applying coding skills to diverse datasets to solve real-world problems—all from your browser, enhancing your practical experience.

Personalized Mentorship

We value mentoring highly. Our program offers one-on-one mentoring and trains individuals to become effective mentors within their fostering growth and development.

Experienced Faculty Members

Learn from top industry experts. With a low student-to-instructor ratio, we ensure close interaction with faculty, providing personalized support throughout your learning journey.

Enhancing Employability

To meet industry needs, mentors at AppliedTech Academy develop talent by providing support and preparing you for the challenges of Industry 4.0.

Internship Certification

Join us for a dynamic 2-month certified internship, immersing yourself in hands-on action as we embark on an exciting new era of technological advancement!

Career Counseling Services

If you're looking for a Data Science & Analytics course with career counseling assistance, this opportunity is perfect for enhancing your soft skills and knowledge!

About the Data Science Certification Course

Data Science Certification Courses: Your Path to Professional Excellence

Our Data Science Certification Course at AppliedTech is meticulously designed to equip individuals with the essential skills and knowledge needed to thrive in the rapidly evolving field of Data Science. Whether you are a beginner or looking to upskill, this course is tailored for those interested in data analysis, machine learning, data visualization, and predictive modeling.

One of the primary advantages of our Data Science Certification Course is its ability to open a wide range of career opportunities. As businesses increasingly adopt data-driven decision-making processes, the demand for skilled professionals who can derive valuable insights from data continues to grow. Data Scientists are highly sought after across various industries, including finance, healthcare, e-commerce, and marketing.

Throughout the course, students will gain expertise in programming languages like Python, as well as skills in data manipulation, cleaning, visualization, statistical analysis, and machine learning algorithms. The curriculum incorporates real-world projects, allowing students to apply their learning to practical problems and scenarios.

Earning a Data Science certification from AppliedTech serves as a valuable asset when entering the job market. Many employers actively seek candidates who have demonstrated their expertise through recognized certifications. Furthermore, our certification can lead to higher salary prospects and increased opportunities for career advancement, making it a strategic step for anyone looking to excel in this dynamic field.

Join us at AppliedTech to elevate your career in Data Science!

Languages and Tools Covered :

Why choose AppliedTech :

At AppliedTech, we envision a future where individuals are empowered to navigate and excel in the rapidly evolving realm of technology. Our dedicated team is committed to revolutionizing the learning experience, instilling innovative thinking and adaptability to keep pace with the ever-changing technological landscape.

Our mission at AppliedTech is to cultivate a culture of continuous learning and development, nurturing individuals from diverse backgrounds, whether rooted in the world of IT or branching out into non-IT domains. We firmly believe that knowledge has no boundaries, and we are dedicated to breaking down barriers to make technology education accessible to all.

Get Ahead with Data Science Certificate!

Certificate of Completion for the 6 Months Program
Internship Certificate from Participating Companies
Letter of Recommendation

Program Eligibility Criteria and Prerequisites :

Ready to be a Data Scientist?

AppliedTech’s Data Science Certification Course is expertly crafted to provide individuals with the essential skills and knowledge needed to excel in the rapidly evolving field of Data Science. Tailored for both beginners and those looking to upskill, the course covers critical areas such as data analysis, machine learning, and data visualization. With a focus on real-world projects, students gain hands-on experience with programming languages like Python, making them highly sought after in industries like finance, healthcare, and marketing. Earning this certification not only enhances employability but also opens doors to higher salary prospects and career advancement.

Enroll Today!

Course Syllabus In Detail :

Basics of Python Programming

Session 1: Python Basics
- About Python
- Python Data Types
- Python Variables
- Python comments
- Python Keywords and Identifiers
- Python User Input
- Python Type conversion
- Python Literals
Session 2: Python Operators + if-else + Loops
- Python Operators
- Python if-else
- Python While Loop
- Python for loop
- Break, continue, pass statement in loops
Session 3: Python Strings
- String indexing
- String slicing
- Common String functions
Assignments and Interview Questions

Python Data Types

Session 4: Python Lists
- Array vs List
- How lists are stored in a memory
- All Operations on List
- List Functions
Session 5: Tuples + Set + Dictionary

- Tuple
- Operations on tuple
- Set functions
Session 6: Dictionary
- Operations on dictionary
- Dictionary functions
Assignments and Interview Questions

Python Functions

Create functions.
Arguments and parameters
args and kwargs
map(), filter(), reduce()
Assignments and Interview Questions

Object Oriented Programming (OOP)

Session 7: OOP Part1
- What is OOP?
- What are classes and Objects?
- Methods vs Functions
- Magic/Dunder methods
- What is the true benefit of constructor?
- Concept of ‘self’
- __str__, __add__, __sub__ , __mul__ , __truediv__
Session 8: OOP Part2
- Encapsulation
- Collection of objects
Session 9: OOP Part3
- Class Relationship
- Inheritance and Inheritance class diagram
- Constructor example
- Types of Inheritance (Single, Multilevel, Hierarchical,Multiple )
- Code example and diamond problem
- Polymorphism
- Method Overriding and Method Overloading
Session on Abstraction
- What is Abstraction?
- Abstract class
3 Interview Questions

Advanced Python

Session 10: File Handling + Serialization & Deserialization
- How File I/O is done
- Writing to a new text file
- append()
- Reading a file -> read() and readline()
- Seek and tell
- Working with Binary file
- Serialization and Deserialization
- JSON module -> dump() and load()
- Pickling
Session 11: Exception Handling
- Syntax/Runtime Error with Examples
- Why we need to handle Exception?
- Exception Handling (Try-Except-Else-Finally)
- Handling Specific Error
- Raise Exception
- Create custom Exception
- Exception Logging
Session 12: Decorators
- Decorators with Examples
Session on Generator
- What is a generator?
- Why to use Generator?
- Yield vs Return
4 Interview Questions

Numpy

Session 13: Numpy Fundamentals
- Numpy Theory
- Numpy array
- Matrix in numpy
- Array operations
- Scalar and Vector operations
Session 14: Advanced Numpy
- Numpy array vs Python List
- Broadcasting
- Mathematical operations in numpy
- Sigmoid in numpy
- Mean Squared Error in numpy
- Various functions like sort, append, concatenate, percentile, flip, Set functions, etc.

Pandas

Session 16: Pandas Series
- What is Pandas?
- Introduction to Pandas Series
- Series Methods
Session 17: Pandas DataFrame
- Introduction Pandas DataFrame
- Creating DataFrame and read_csv()
- Selecting cols and rows from dataframe
- Filtering a Dataframe
- Adding new columns
Session 18: Important DataFrame Methods
- Sort, index, reset_index, isnull, dropna, fillna, drop_duplicates, value_counts, apply etc.

Advanced Pandas

Session 19: GroupBy Object
- What is GroupBy?
- Applying builtin aggregation fuctions on groupby objects
Session 20: Merging, Joining, Concatenating
- Pandas concat method
- Merge and join methods
- Practical implementations
Session 21: MultiIndex Series and DataFrames
Session on Pandas Case Study

Data Visualization

Session 23: Plotting Using Matplotlib
- Get started with Matplotlib
- Plotting simple functions, labels, legends, multiple plots
- About scatter plots
- Bar chart
- Histogram
- Pie chart
- Changing styles of plots

Data Visualization Continued

Session 25: Plotting Using Seaborn
- Why seaborn?
- Categorical Plots
- Stripplot
- Swarmplot
- Categorical Distribution Plots
- Boxplot
- Violinplot
- Barplot

Data Preprocessing and Analysis Process

Session on Data Cleaning and Data Preprocessing Case Study
- Quality issues
- Tidiness issues
- Data Cleaning
Session 29: Exploratory Data Analysis (EDA)
- Introduction to EDA
- Why EDA?
- Steps for EDA
- Univariate, Bivariate Analysis
- Feature Engineering
Data Preprocessing steps.

SQL Basics

Session 30: Database Fundamentals
- Introduction to Data and Database
- CRUD operations
- Types of Database
- MySQL workbench
- DDL ,DML ,DQL ,DCL Commands
- Selecting & Retrieving Data with SQL
- Filtering, Sorting, and Calculating Data with SQL
- Sub Queries and Joins in SQL

Descriptive Statistics

Session 38: Descriptive Statistics Part 1
- What is Statistics?
- Types of Statistics
- Population vs Sample
- Types of Data
- Measures of central tendency
- Measure of Dispersion
- Quantiles and Percentiles
- Five Number Summary
- Boxplots
- Scatterplots
- Covariance
- Correlation
Probability Distribution Functions (PDF, CDF, PMF)
- Random Variables
- Probability Distributions
- Probability Distribution Functions and its types
- Probability Mass Function (PMF)
- Cumulative Distribution Function (CDF) of PMF
- Probability Density Function (PDF)
- Density Estimation
- Parametric and Non-parametric Density Estimation
- Kernel Density Estimate (KDE)
- Cumulative Distribution Function (CDF) of PDF.

Probability Distributions

Session 41: Normal Distribution
- How to use PDF in Data Science?
- 2D density plots
- Normal Distribution (importance, equation, parameter, intuition)
- Standard Normal Variate (importance, z-table, empirical rule)
- Skewness
- Use of Normal Distribution in Data Science
Session 42: Non-Gaussian Probability Distributions
- Kurtosis and Types
- Transformation
  - Mathematical Transformation
  - Log Transform

- - Reciprocal Transform / Square or sqrt Transform
  - Power Transformer
  - Box-Cox Transform

Inferential Statistics

Session 43: Central Limit Theorem
- Bernouli Distribution
- Binomial Distribution
- Intuition of Central Limit Theorem (CLT)
- CLT in code
Session 44: Confidence Intervals
- Confidence Interval
  - Ways to calculate CI
  - Applications of CI
  - Confidence Intervals in code

Hypothesis Testing

Session 45: Hypothesis Testing (Part 1)
- Key idea of hypothesis testing
- Null and alternate hypothesis
- Steps in Hypothesis testing
- Performing z-test
- Rejection region and Significance level
- Type-1 error and Type-2 Error
- One tailed vs. two tailed test
- Applications of Hypothesis Testing
- Hypothesis Testing in Machine Learning
Session 46: Hypothesis Testing (Part 2) | p-value and t-tests
- What is p-value?
- Interpreting p-value
- T-test
- Types of t-test
  - Single sample t-Test
  - Independent 2-sample t-Test

- - Paired 2 sample t-Test
  - Code examples of all of the above

Session on Chi-square test
- Chi-square test
- Goodness of fit test (Steps, Assumptions, Examples)
- Test for Independence (Steps, Assumptions, Examples)
- Applications in machine learning
Session on ANOVA
- F-distribution
- One/Two-way ANOVA

Linear Algebra

Session on Tensors | Linear Algebra part 1(a)
- What are tensors?
- 0D, 1D and 2D Tensors
- Nd tensors
- Example of 1D, 2D, 3D, 4D, 5D tensors
Session on Vectors | Linear Algebra part 1(b)
- What is Linear Algebra?
- What are Vectors?
- Vector example in ML
- Row and Column vector
- Distance from Origin
- Euclidean Distance
- Scalar Addition/Subtraction (Shifting)
- Vector Addition/Subtraction
- Dot product
- Angle between 2 vectors
Linear Algebra Part 2 | Matrices (computation)
- What are matrices?
- Types of Matrices
- Matrix Equality
- Scalar Operation
- Matrix Addition, Subtraction, multiplication
- Transpose of a Matrix
- Determinant
- Inverse of Matrix
Linear Algebra Part 3 | Matrices (Intuition)
- Basis vector
- Linear Transformations
- Linear Transformation in 3D
- Matrix Multiplication as Composition
- Determinant and Inverse
- Transformation for non-square matrix?

Linear Regression

Session 48: Introduction to Machine Learning
- About Machine Learning (History and Definition)
- Types of ML
  - Supervised Machine Learning
  - Unsupervised Machine Learning
  - Semi supervised Machine Learning
  - Reinforcement Learning
- Batch/Offline Machine Learning
- Instance based learning
- model-based learning
- Instance vs model-based learning
- Challenges in ML
  - Data collection
  - Insufficient/Labelled data
  - Non-representative date
  - Poor quality data
  - Irrelevant features
  - Overfitting and Underfitting
  - Offline learning
  - Cost
- Machine Learning Development Life-cycle
- Different Job roles in Data Science
- Framing a ML problem | How to plan a Data Science project
Session 49: Simple Linear regression
- Introduction and Types of Linear Regression
- Intuition of simple linear regression
- How to find m and b?
- Regression Metrics
- MAE, MSE, RMSE, R2 score, Adjusted R2 score
Session 50: Multiple Linear Regression
- Introduction to Multiple Linear Regression (MLR)
- Mathematical Formulation of MLR
- Error function of MLR
- Session on Polynomial Regression
  - Why we need Polynomial Regression?
  - Formulation of Polynomial Regression
- Session on Assumptions of Linear Regression
- Session 53: Multicollinearity
  - What is multicollinearity?
  - How to detect and remove Multicollinearity
  - Correlation
  - VIF (Variance Inflation Factor)

Basics of Python Programming

Session 1: Python Basics
- About Python
- Python Data Types
- Python Variables
- Python comments
- Python Keywords and Identifiers
- Python User Input
- Python Type conversion
- Python Literals
Session 2: Python Operators + if-else + Loops
- Python Operators
- Python if-else
- Python While Loop
- Python for loop
- Break, continue, pass statement in loops
Session 3: Python Strings
- String indexing
- String slicing
- Common String functions
Assignments and Interview Questions

Python Data Types

Session 4: Python Lists
- Array vs List
- How lists are stored in a memory
- All Operations on List
- List Functions
Session 5: Tuples + Set + Dictionary

- Tuple
- Operations on tuple
- Set functions
Session 6: Dictionary
- Operations on dictionary
- Dictionary functions
Assignments and Interview Questions

Python Functions

Create functions.
Arguments and parameters
args and kwargs
map(), filter(), reduce()
Assignments and Interview Questions

Object Oriented Programming (OOP)

Session 7: OOP Part1
- What is OOP?
- What are classes and Objects?
- Methods vs Functions
- Magic/Dunder methods
- What is the true benefit of constructor?
- Concept of ‘self’
- __str__, __add__, __sub__ , __mul__ , __truediv__
Session 8: OOP Part2
- Encapsulation
- Collection of objects
Session 9: OOP Part3
- Class Relationship
- Inheritance and Inheritance class diagram
- Constructor example
- Types of Inheritance (Single, Multilevel, Hierarchical,Multiple )
- Code example and diamond problem
- Polymorphism
- Method Overriding and Method Overloading
Session on Abstraction
- What is Abstraction?
- Abstract class
3 Interview Questions

Advanced Python

Session 10: File Handling + Serialization & Deserialization
- How File I/O is done
- Writing to a new text file
- append()
- Reading a file -> read() and readline()
- Seek and tell
- Working with Binary file
- Serialization and Deserialization
- JSON module -> dump() and load()
- Pickling
Session 11: Exception Handling
- Syntax/Runtime Error with Examples
- Why we need to handle Exception?
- Exception Handling (Try-Except-Else-Finally)
- Handling Specific Error
- Raise Exception
- Create custom Exception
- Exception Logging
Session 12: Decorators
- Decorators with Examples
Session on Generator
- What is a generator?
- Why to use Generator?
- Yield vs Return
4 Interview Questions

Numpy

Session 13: Numpy Fundamentals
- Numpy Theory
- Numpy array
- Matrix in numpy
- Array operations
- Scalar and Vector operations
Session 14: Advanced Numpy
- Numpy array vs Python List
- Broadcasting
- Mathematical operations in numpy
- Sigmoid in numpy
- Mean Squared Error in numpy
- Various functions like sort, append, concatenate, percentile, flip, Set functions, etc.

Pandas

Session 16: Pandas Series
- What is Pandas?
- Introduction to Pandas Series
- Series Methods
Session 17: Pandas DataFrame
- Introduction Pandas DataFrame
- Creating DataFrame and read_csv()
- Selecting cols and rows from dataframe
- Filtering a Dataframe
- Adding new columns
Session 18: Important DataFrame Methods
- Sort, index, reset_index, isnull, dropna, fillna, drop_duplicates, value_counts, apply etc.

Advanced Pandas

Session 19: GroupBy Object
- What is GroupBy?
- Applying builtin aggregation fuctions on groupby objects
Session 20: Merging, Joining, Concatenating
- Pandas concat method
- Merge and join methods
- Practical implementations
Session 21: MultiIndex Series and DataFrames
Session on Pandas Case Study

Data Visualization

Session 23: Plotting Using Matplotlib
- Get started with Matplotlib
- Plotting simple functions, labels, legends, multiple plots
- About scatter plots
- Bar chart
- Histogram
- Pie chart
- Changing styles of plots

Data Visualization Continued

Session 25: Plotting Using Seaborn
- Why seaborn?
- Categorical Plots
- Stripplot
- Swarmplot
- Categorical Distribution Plots
- Boxplot
- Violinplot
- Barplot

Data Preprocessing and Analysis Process

Session on Data Cleaning and Data Preprocessing Case Study
- Quality issues
- Tidiness issues
- Data Cleaning
Session 29: Exploratory Data Analysis (EDA)
- Introduction to EDA
- Why EDA?
- Steps for EDA
- Univariate, Bivariate Analysis
- Feature Engineering
Data Preprocessing steps.

SQL Basics

Session 30: Database Fundamentals
- Introduction to Data and Database
- CRUD operations
- Types of Database
- MySQL workbench
- DDL ,DML ,DQL ,DCL Commands
- Selecting & Retrieving Data with SQL
- Filtering, Sorting, and Calculating Data with SQL
- Sub Queries and Joins in SQL

Descriptive Statistics

Session 38: Descriptive Statistics Part 1
- What is Statistics?
- Types of Statistics
- Population vs Sample
- Types of Data
- Measures of central tendency
- Measure of Dispersion
- Quantiles and Percentiles
- Five Number Summary
- Boxplots
- Scatterplots
- Covariance
- Correlation
Probability Distribution Functions (PDF, CDF, PMF)
- Random Variables
- Probability Distributions
- Probability Distribution Functions and its types
- Probability Mass Function (PMF)
- Cumulative Distribution Function (CDF) of PMF
- Probability Density Function (PDF)
- Density Estimation
- Parametric and Non-parametric Density Estimation
- Kernel Density Estimate (KDE)
- Cumulative Distribution Function (CDF) of PDF.

Probability Distributions

Session 41: Normal Distribution
- How to use PDF in Data Science?
- 2D density plots
- Normal Distribution (importance, equation, parameter, intuition)
- Standard Normal Variate (importance, z-table, empirical rule)
- Skewness
- Use of Normal Distribution in Data Science
Session 42: Non-Gaussian Probability Distributions
- Kurtosis and Types
- Transformation
  - Mathematical Transformation
  - Log Transform

- - Reciprocal Transform / Square or sqrt Transform
  - Power Transformer
  - Box-Cox Transform

Inferential Statistics

Session 43: Central Limit Theorem
- Bernouli Distribution
- Binomial Distribution
- Intuition of Central Limit Theorem (CLT)
- CLT in code
Session 44: Confidence Intervals
- Confidence Interval
  - Ways to calculate CI
  - Applications of CI
  - Confidence Intervals in code

Hypothesis Testing

Session 45: Hypothesis Testing (Part 1)
- Key idea of hypothesis testing
- Null and alternate hypothesis
- Steps in Hypothesis testing
- Performing z-test
- Rejection region and Significance level
- Type-1 error and Type-2 Error
- One tailed vs. two tailed test
- Applications of Hypothesis Testing
- Hypothesis Testing in Machine Learning
Session 46: Hypothesis Testing (Part 2) | p-value and t-tests
- What is p-value?
- Interpreting p-value
- T-test
- Types of t-test
  - Single sample t-Test
  - Independent 2-sample t-Test

- - Paired 2 sample t-Test
  - Code examples of all of the above

Session on Chi-square test
- Chi-square test
- Goodness of fit test (Steps, Assumptions, Examples)
- Test for Independence (Steps, Assumptions, Examples)
- Applications in machine learning
Session on ANOVA
- F-distribution
- One/Two-way ANOVA

Linear Algebra

Session on Tensors | Linear Algebra part 1(a)
- What are tensors?
- 0D, 1D and 2D Tensors
- Nd tensors
- Example of 1D, 2D, 3D, 4D, 5D tensors
Session on Vectors | Linear Algebra part 1(b)
- What is Linear Algebra?
- What are Vectors?
- Vector example in ML
- Row and Column vector
- Distance from Origin
- Euclidean Distance
- Scalar Addition/Subtraction (Shifting)
- Vector Addition/Subtraction
- Dot product
- Angle between 2 vectors
Linear Algebra Part 2 | Matrices (computation)
- What are matrices?
- Types of Matrices
- Matrix Equality
- Scalar Operation
- Matrix Addition, Subtraction, multiplication
- Transpose of a Matrix
- Determinant
- Inverse of Matrix
Linear Algebra Part 3 | Matrices (Intuition)
- Basis vector
- Linear Transformations
- Linear Transformation in 3D
- Matrix Multiplication as Composition
- Determinant and Inverse
- Transformation for non-square matrix?

Linear Regression

Session 48: Introduction to Machine Learning
- About Machine Learning (History and Definition)
- Types of ML
  - Supervised Machine Learning
  - Unsupervised Machine Learning
  - Semi supervised Machine Learning
  - Reinforcement Learning
- Batch/Offline Machine Learning
- Instance based learning
- model-based learning
- Instance vs model-based learning
- Challenges in ML
  - Data collection
  - Insufficient/Labelled data
  - Non-representative date
  - Poor quality data
  - Irrelevant features
  - Overfitting and Underfitting
  - Offline learning
  - Cost
- Machine Learning Development Life-cycle
- Different Job roles in Data Science
- Framing a ML problem | How to plan a Data Science project
Session 49: Simple Linear regression
- Introduction and Types of Linear Regression
- Intuition of simple linear regression
- How to find m and b?
- Regression Metrics
- MAE, MSE, RMSE, R2 score, Adjusted R2 score
Session 50: Multiple Linear Regression
- Introduction to Multiple Linear Regression (MLR)
- Mathematical Formulation of MLR
- Error function of MLR
- Session on Polynomial Regression
  - Why we need Polynomial Regression?
  - Formulation of Polynomial Regression
- Session on Assumptions of Linear Regression
- Session 53: Multicollinearity
  - What is multicollinearity?
  - How to detect and remove Multicollinearity
  - Correlation
  - VIF (Variance Inflation Factor)

Testimonials

What they say

"The Data Science course at AppliedTech exceeded my expectations! The hands-on projects and real-world applications made learning practical and engaging. The instructors were incredibly supportive and ensured every concept was crystal clear. I feel well-equipped to dive into the data science field now."

"As someone coming from a non-IT background, I was initially nervous about learning data science. But AppliedTech’s personalized approach and expert mentorship made the transition seamless. The course was challenging yet rewarding and I now feel confident in applying these skills to real-world problems."

"I am thrilled to have completed the Data Science certificate course in this academy. The course was well-organized, and the mentors were industry professionals who shared valuable insights. The project-based learning helped me apply what I learned, and the flexible schedule fit perfectly with my work commitments.”

"AppliedTech’s Data Science course stands out for its practical approach to teaching. The focus on projects and real-life datasets really helped me grasp the core concepts of data analysis and machine learning. The mentorship from experienced professionals added immense value to the overall experience."

"This course was a game-changer for me. The Data Science program not only provided top-notch content but also personalized guidance. The combination of theory and practical application prepared me for the job market. This course really helped me to crack an an interview."

Data Science Course FAQs

What steps should I take to become a Data Scientist?

To become a Data Scientist, it’s essential to build a solid foundation in mathematics, statistics, and computer science. Pursuing a degree in Data Science or a related field can be beneficial, along with gaining practical experience through internships or hands-on projects.

What is the average salary for a Data Scientist?

The salary of a Data Scientist can fluctuate significantly based on factors such as location, experience, industry, and skill set. Reports indicate that the average annual salary for a Data Scientist in the United States is approximately $113,309. However, this figure can vary widely, ranging from around $76,000 to over $170,000 per year, depending on these factors.

Which major companies are currently hiring Data Scientists?

Numerous major companies across diverse industries are actively seeking to hire Data Scientists. Tech giants like Amazon, Google, Microsoft, Facebook, IBM, and Apple are among the largest employers in this field. Additionally, financial institutions such as JPMorgan Chase, Goldman Sachs, and American Express are in need of Data Scientists, along with healthcare companies like Johnson & Johnson and Merck. Retail giants such as Walmart, Target, and Macy’s are also looking for skilled professionals. Beyond these large corporations, many startups and smaller companies are actively recruiting Data Scientists to enhance their data-driven decision-making capabilities.

What are the primary industries that utilize Data Science?

Data science is widely applicable across various industries, including finance, healthcare, retail and e-commerce, marketing, technology, and education. These sectors leverage data science techniques for a multitude of purposes, such as risk management, drug discovery, customer analytics, product development, and informed decision-making.

Will this Data Science course teach me the fundamentals from the ground up?

Absolutely, this Data Science course is tailored for beginners, offering a thorough foundation in Data Science concepts and techniques.

How will I receive the course materials after enrolling in the Data Science program?

You will receive Data Science course material through our Learning Management System.

What are the key features of the AppliedTech Data Science course?

Key features of the AppliedTech Data Science course include practical hands-on training, engaging real-world projects, and expert mentorship from industry professionals.

How long is the validity period of the course completion certificate?

The course completion certificate is valid for a lifetime and does not have an expiration date.

What is the enrollment process for the Data Scientist course?

To enroll in the Data Science course, visit the AppliedTech website and complete the enrollment form.

Does AppliedTech offer practice exams related to this Data Scientist course?

Yes, AppliedTech offers practice exams related to the Data Science course to help you prepare effectively for assessments and certifications.