Data Science – Basic Course


Industry-based projects with real-time completion

Interactive live session and doubt clearing

100% job assistance with interview and placement guidance

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Data Science Basic Course Description

Experience and learn concepts needed by data scientists. In this course, you will get the basic knowledge needed to start off your Data Science journey. You will understand what Data Science is and will learn different aspects of it. Upon completing this course, you will be able to prepare your data for analysis, perform basic visualization of data and model your data. All Anexas’s courses emphasize practicality and hence include many exercises and real-time projects.

Data Science Basic Course Benefits

After becoming a data scientist, your life will change completely. Better salaries, advanced skill sets, better job profiles, and influential networks are just a few of the benefits of the Anexas Basic Data Science certification. Given below is the list of benefits you will receive after doing your training with Anexas and becoming a certified Data Scientist:
  • Interactive online live training with industry experts.
  • Lifetime dashboard access to course materials.
  • Content-rich study material.
  • Free eBooks with worked out projects.
  • Project guidance
  • Real-time project completion during the training.
  • Participants will be certified by ANEXAS Europe.
  • A soft and hard copy of the certificate.
  • International recognition.
  • Learn from the best trainers.
  • Tools required for the training.
  • Open book online test for certification.
  • 100% Pass rate guaranteed.
  • Lifetime membership to Anexas Alumni group.
  • Get a chance to win cashback benefits.
  • Online Line Customer support 24*7.

100% Job Assistance

Anexas Data Science course comes with 100% job assistance. Learn Data Science with Anexas and advance your career with the most reputed Data Science job roles.

Benefits of Job assistance

  • Get Placement guidance. 
  • Prep Interview skills with real-time industry-based Q&A. 
  • Learn Personality Development. 
  • Fill in any gap years in your experience. 
  • Gain from Anexas Internship program.

Eligibility criteria

  • Complete at least basic and Intermediate level in Data Science course by Anexas. 
  • Fresher or Experienced, anyone can apply. 
  • Programmers or Non-programmers, anyone can apply.

Course Duration

45 Hrs


Study Material

Data Science Basic Training Options

Self Learning

  • 45 hours of Recorded Sessions for course completion
  • Training Certificate from Anexas Europe Certification (AEC)
  • Lifetime access to study material
  • Lifetime access to learning videos

Online Live Training

  • 45 hours of course completion
  • Live interaction with instant query clarification
  • Continuous project guidance during the sessions
  • Global recognition
  • Trained Data Scientist (Basic) Certificate from Anexas Europe
  • Access to learning videos
  • Diverse training batch
  • Flexible timings

Upcoming Batch

Weekday Batch: 19th May

Weekend Batch: Contact [email protected]

Data Science Basic Course Content

  • Installation - Anaconda, Pycharm, Virtualenv
  • Introduction to python
  • Basic Syntax, comments, Variables
  • Data Types, Numbers, Casting, Strings, Booleans
  • Operators, Lists, Tuples, Sets, Dictionaries
  • If...Else, While Loops, For Loops
  • Functions, Lambda, Arrays
  • Arrays, Classes/Objects, Inheritance, Iterators
  • Scope, Modules, Dates, Math, JSON
  • PIP, Try...Except, User InputP, String Formatting
  • File Handling, Read Files, Write/Create Files, Delete Files

  • Ndarray, Data types, Array Attributes, Indexing and Slicing
  • Array manipulation, Binary operator, String Function
  • Arithmetic, Statistical, Matrix, linear algebra, sort, search, countings

  • Data manipulation, Viewing, selection, grouping, merging, joining, concatenation
  • Working with text data, visualization, CSV, XLSX, SQL data puling, operations

  • Statistics, Linear algebra, models, special functions, optimization
  • Probability & Stats Applications

  • Basic Probability, Random experiments, Conditional Probability, Independent Events,
  • Bayes theorem, Permutation, combination
  • Random variable , Discrete/Continous RV, PDF, PMF, CDF
  • Joint Probability Distribution, Conversion techniques, EV, varience, SD
  • Covariance, Correlation, Chebyshev Inequality, Law of Large number
  • Central limit Theorem, Percent & Quantiles, Moments
  • Skewness & Kurtosis, Gaussian, Binomial, Standard Normal, Distribution
  • Poisson, Multinomial, Hypergeometric, Uniform, Exponential Distribution

  • [Mean, median, mode ](Sample/population), Expected values, Variance, standard deviation
  • Sampling distribution, Frequency distribution, Estimation Theory
  • confidence interval, Maximum Likelihood Estimation
  • Hypothesis Testing - Chi-Square, Student's T, F Distribution, Z test
  • Hypothesis Testing - Type-I, Type- II, p Values, Relationship between NULL & Alternative
  • Least Square Methods - Numerical

  • Data Cleaning - Handling Missing Values(Data Imputation), Dealing with Noisy data(Binning Technique)
  • Advance Data cleaning - Will be referred while Regression, clustering topics
  • Data Transformation Techniques- Normalization (minmax, log transform, z-score transform etc.), Attribute Selection, Discretization,Concept Hierarchy Generation
  • Data Reduction: Data Cube Aggregation, Numerosity Reduction, Dimensionality Reduction

  • Data Mapping, Charts, Glyphs, Parallel Coordinates, Stacked Graphs
  • Bar, Pie, Line Charts, bubbles, geo maps. Gauge, whisker charts, Heatmaps, scatterplots, plottings images, videos, motion charts, performing EDA
  • Building Dashboard - Live implementation - PowerBI

  • Implementation of Numerical intuitions
  • Regression basics: Relationship between attributes using Covariance and Correlation
  • Relationship between multiple variables: Regression (Linear, Multivariate) in prediction.
  • Residual Analysis: Identifying significant features, feature reduction using AIC, multi-collinearity

  • Polynomial Regression
  • Regularization methods
  • Lasso, Ridge and Elastic nets
  • Categorical Variables in Regression

  • Logit function and interpretation
  • Types of error measures (ROCR)
  • Logistic Regression in classification

  • Distance measures - euclidean distance
  • Different clustering methods (Distance, Density, Hierarchical)
  • Iterative distance-based clustering;
  • Dealing with continuous, categorical values in K-Means
  • Constructing a hierarchical cluster
  • K-nearest neighbours, K-Medoids, k-Mode and density-based clustering
  • BIRCH, DBSCAN, Mean Shift, Spectral Clustering, Gaussian Mixture Model

  • The applications of Association Rule Mining: Market Basket, Recommendation Engines, etc.
  • A mathematical model for association analysis; Large item sets; Association Rules
  • Apriori: Constructs large item sets with mini sup by iterations; Analysis discovered association rules;
  • Application examples; Association analysis vs. classification
  • FP-trees
  • PageRank

Enquiry Form

Data Science Basic FAQ

Our comprehensive course will provide you with a deep understanding of Data Science and cover various aspects of this field. By the end of the course, you will gain the skills to effectively prepare and analyze data, create impactful data visualizations, and develop data models. Expand your knowledge and capabilities in Data Science with us.

  • Total 7 years of experience in Data Science, Machine Learning, Deep Learning & Application Development.
  • Contributed to 46+ Patents for different countries (US, China, Europe)
  • Published research paper at the National Library of Medicine.
  • Currently a mentor at the University of Texas at Austin.
  • Serving as Senior Data Scientist.

  • High demand
  • Infinite career opportunities
  • Highest pay due to high exigency
  • Recruited by top companies
  • Flexibility to switch domains
  • Opportunity to pursue self-career

Data Science is a highly lucrative career option, with Data Scientists earning an average of $114,000 USD in the United States and 900,000 INR in India annually, as per recent research. Explore a range of rewarding job roles in the field:  

  • Data Scientist 
  • Python Programmer 
  • Machine Learning Engineer 
  • Data Analyst 
  • Data Engineer

Our comprehensive course will provide you with a deep understanding of Data Science and cover various aspects of this field. By the end of the course, you will gain the skills to effectively prepare and analyze data, create impactful data visualizations, and develop data models. Expand your knowledge and capabilities in Data Science with us.

Yes! Our training fees cover all associated costs, including certification, examination, study materials, and tools/software expenses. There are no additional charges, ensuring a hassle-free learning experience for you.

At Anexas, we provide convenient payment options to suit your preference. Choose from the following methods:  

  • Card Payment 
  • Net Banking 
  • Cash

Cancellation Policy:

If a delegate cancels the training 72 hours prior to the scheduled start, an administration fee of 10% will be deducted from the refund. However, if the cancellation occurs after this timeframe, no refund will be provided.  

Rescheduling Option: 

Once the payment is made, delegates have the flexibility to postpone their training date and join another batch at no extra cost. We aim to accommodate your schedule and ensure a seamless learning experience.

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