Data scientists are in high demand across various industries, making it a lucrative career choice
You'll learn how to make informed decisions based on data analysis, leading to better business outcomes
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DATA SCIENCE COURSE
A data science course is designed to equip students or aspiring data scientists with the knowledge and skills required to analyze and interpret comple...Read more
Helix Tech
Helix Tech
Helix Tech
- Hands on Training
- Flexible Timings
- Industry Based Training
- Experienced Experts
- Affordable Fees
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Course Highlights
DATA SCIENCE COURSE
A data science course is designed to equip students or aspiring data scientists with the knowledge and skills required to analyze and interpret complex data to extract valuable insights and make data-driven decisions. These courses typically cover a wide range of topics in statistics, programming, machine learning, data visualization, and data management. Here's an overview of what you might expect in a data science course. Students learn how to acquire, clean, and preprocess data to prepare it for analysis. This involves dealing with missing values, outliers, and data inconsistencies. Fundamental statistical concepts and probability theory are covered to understand data distributions and make inferences from data.
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At Helix Tech IT Services Inc, we believe that the success of a company is rooted in the talents and aspirations of its employees. We are committed to creating a positive impact on the tech industry by bridging the gap between top tech talent and companies while empowering job seekers to achieve their career aspirations. We believe in creating a thriving ecosystem where technology and innovation comes to life.
I'M READY TO GET INCourse Curriculum
- Introduction to Data Science & Evolution of Data Science
- Difference between Data analyst & Data Scientist
- Roles & Responsibilities of Data Scientist
- Difference between Supervised & Unsupervised learning
- What is machine learning
- Deep learning and Artificial Intelligence
- Probability
- Bayesian Inference
- Hypothesis testing
- Descriptive Statistics
- Inferential Statistics
- Hypothesis testing
- Statistical Distribution(Discrete and continuous)
- Installation of Anaconda Framework
- How to work with Jupiter notebook and Spyder IDE’s
- Python Data type
- What is linear regression?
- What is logistic regression?
- Difference between Linear and Logistic
- Difference between Regression and Classification
- Building a model using Linear and Logistic Regression
- What is clustering?
- Difference between K-means and KNN
- Different Use cases of clustering
- Building a model using K-means and KNN
- Data Exploration and Cleaning
- Optimization Techniques
- Natural Language Processing (NLP)
- Data Wrangling
- Time Series Analysis
- Big Data Processing
- Model Deployment
- Data Ethics and Privacy
- Descriptive and Inferential Statistics
- Classification
- Data Ethics and Privacy
- Data Storytelling
- Data Science Tools
- Domain Knowledge
- Ensemble Methods
- A/B Testing
Professional Certificate
Beginner level
No previous experience necessary
Course Session
20 Days 1 Hours per day
Flexible schedule
Learn at your own pace
- Data Analysis
- Statistics
- Machine Learning
- Data Visualization
- Data Scientist
- Machine Learning Engineer
- Big Data Engineer
FAQ
The training is designed to describe the main goals and objectives of the training, such as enhancing skills, improving knowledge, etc.
This training is intended for specify the target audience, such as beginners, professionals, specific job roles, etc.
List any required prerequisites, such as prior knowledge, experience, or skills that participants should have before taking the training.
Interview Question
Data Science combines statistics, maths, specialised programs, artificial intelligence, machine learning etc. Data Science is simply the application of specific principles and analytic techniques to extract information from data used in strategic planning, decision making, etc. Simply, data science means analysing data for actionable insights.
Logistic regression measures the relationship between the dependent variable (our label of what we want to predict) and one or more independent variables (our features) by estimating probability using its underlying logistic function (sigmoid).
The Dimensionality reduction refers to the process of converting a data set with vast dimensions into data with fewer dimensions (fields) to convey similar information concisely.
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