Data Science Online course
Enroll in the most comprehensive Data Science online course in the market, meticulously designed to cover the entire Data Science lifecycle. This course encompasses a wide range of critical concepts, from Data Collection, Data Extraction, and Data Cleansing to Data Exploration, Data Transformation, and Feature Engineering. It also delves into Data Integration, Data Mining, building Prediction models, Data Visualization, and deploying solutions to customers.
Data Science Online course
Master Data Science with Smart Learn IT’s Online Programs
Discover the most comprehensive Data Science online course in the market, offered by Smart Learn IT. Our program covers the entire Data Science lifecycle, including Data Collection, Data Extraction, Data Cleansing, Data Exploration, Data Transformation, Feature Engineering, Data Integration, Data Mining, building Prediction models, Data Visualization, and deploying solutions to customers. This Data Science online course is meticulously designed to provide a deep dive into every aspect of Data Science, ensuring you gain both theoretical knowledge and practical skills.
Accredited Data Analyst Certificate for Aspiring Professionals
Smart Learn IT is renowned for providing the best Data Science online training, which is considered top-tier in the industry. Our Data Science online course includes over 400+ participants, offering an accredited Data Analyst Certificate as part of the training program. This certificate is a testament to the quality and depth of our Data Science training, making it an ideal choice for aspiring professionals looking to excel in the field.
Explore Our Career Courses in Data Science
Embark on a journey to success with Smart Learn IT’s Data Science online course. Our career-focused courses are designed to cater to both beginners and experienced professionals. As part of our Data Science online training, you will engage in hands-on projects and real-world scenarios, ensuring that you are well-equipped to tackle the challenges of the Data Science industry. Join us to transform your career and become a skilled data analyst with our comprehensive online programs.
To apply for the Data Science Certification Training, you need to either:
- You need to have a good foundation in mathematical concepts like linear algebra, calculus, probability and statistics
- You need to know at least one programming language like Python or R. You need to have a good understanding of OOPs concepts, algorithms and data structures.
- You need to have some basic data analysis and data visualisation skills.
We guarantee learning at your convenience & pace.
- Instant Access:
Get instant access to self-paced training after signup. - Streaming video recording:
Watch lessons any time at your schedule, free recording. - Exercises:
Practical exercises help you test what you are learning as you go. - Free Demo:
Sign up for free demo to check whether the course is right for you and interact with the faculty live. - Experienced Trainers:
We only hire the industry’s best trainers - Live free interactive web sessions:
Ask the Expert Shell Scripting trainers about the career prospects and clarify your questions any time after you complete the course. - Structured Curriculum Schedule:
Progress with your complete daily interactive lessons and assignments. - Faculty Mentoring:
Turn in daily and weekly homework for personalized feedback from faculty. - Virtual Office Hours:
Live interaction with the faculty and other students around the world. - Hands on Live Projects:
Work on live lab sessions to tackle real-world projects. Get 100% faculty guidance and ratings.
Data Science Online Training
Introduction to Python Programming
- Introduction to Data Science
- Introduction to Python
- Basic Operations in Python
- Variable Assignment
- Functions: in-built functions, user defined functions
- Condition: if, if-else, nested if-else, else-if
Data Structure – Introduction
- List: Different Data Types in a List, List in a List
- Operations on a list: Slicing, Splicing, Sub-setting
- Condition(true/false) on a List
- Applying functions on a List
- Dictionary: Index, Value
- Operation on a Dictionary: Slicing, Splicing, Sub-setting
- Condition(true/false) on a Dictionary
- Applying functions on a Dictionary
- Numpy Array: Data Types in an Array, Dimensions of an Array
- Operations on Array: Slicing, Splicing, Sub-setting
- Conditional(T/F) on an Array
- Loops: For, While
- Shorthand for For
- Conditions in shorthand for For
Basics of Statistics
- Statistics & Plotting
- Seabourn & Matplotlib – Introduction
- Univariate Analysis on a Data
- Plot the Data – Histogram plot
- Find the distribution
- Find mean, median and mode of the Data
- Take multiple data with same mean but different sd, same mean and sd but different kurtosis: find mean, sd, plot
- Multiple data with different distributions
- Bootstrapping and sub-setting
- Making samples from the Data
- Making stratified samples – covered in bivariate analysis
- Find the mean of sample
- Central limit theorem
- Plotting
- Hypothesis testing + DOE
- Bivariate analysis
- Correlation
- Scatter plots
- Making stratified samples
- Categorical variables
- Class variable
Use of Pandas
- File I/O
- Series: Data Types in series, Index
- Data Frame
- Series to Data Frame
- Re-indexing
- Operations on Data Frame: Slicing, Splicing (also Alternate), Sub-setting
- Pandas
- Stat operations on Data Frame
- Reading from different sources
- Missing data treatment
- Merge, join
- Options for look and feel of data frame
- Writing to file
- db operations
Data Manipulation & Visualization
- Data Aggregation, Filtering and Transforming
- Lamda Functions
- Apply, Group-by
- Map, Filter and Reduce
- Visualization
- Matplotlib, pyplot
- Seaborn
- Scatter plot, histogram, density, heat-map, bar charts
Linear Regression
- Regression – Introduction
- Linear Regression: Lasso, Ridge
- Variable Selection
- Forward & Backward Regression
Logistic Regression
- Logistic Regression: Lasso, Ridge
- Naive Bayes
Unsupervised Learning
- Unsupervised Learning – Introduction
- Distance Concepts
- Classification
- k nearest
- Clustering
- k means
- Multidimensional Scaling
- PCA
Random Forest
- Decision trees
- Cart C4.5
- Random Forest
- Boosted Trees
- Gradient Boosting
SVM
- SVM – Introduction
- Hyper-plane
- Hyper-plane to segregate to classes
- Gamma
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