Data Science - Basic Course

4.5/5

Industry-based projects with real-time completion

Interactive live session and doubt clearing

100% job assistance with interview and placement guidance

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.

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

Certificate

Study Material

Training Options

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: Contact enquiry@anexas.net

Weekend Batch: 17th September

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

Industry Projects

FAQ

Data Science is very much favoured in almost all industries today because of its vast usage in data analytics, predictions, statistical facts and figures and trends. Due to advancements in technology, the importance of Data Science has increased and will be more in the upcoming years. As a Data Scientist, you will have uncountable opportunities in different industries and almost all companies as it is multi-disciplinary and every industry will be in need.
Data Science is your own path to having your dreams come true as Data Scientists are needed not only just in every leading industry but also in leading cities in the world. Data Science is your coupon to get into top MNC companies. The career scope as a Data Scientist is infinite and it can only be fully utilized by learning and gaining expertise in the skills with our Data Science certification.

  • 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

According to recent research, a Data Scientist earns an average of $114,000 USD in the United States and 900,000 INR in India every year. Data Science is the most fruitful career option nowadays. Some common job roles are-

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

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.

Yes. Once you pay the fees for the training, all the other costs, such as certification cost, examination cost, study material costs, tools and software costs, will be included and no extra charges will be levied.

Anexas offers the following payment options:

  • Card payment
  • Net Banking
  • Cash

  • If the cancellation is done by the delegate 72 hours before the start of the training, 10% will be deducted as an administration fee.
  • If the cancellation is done by the delegate after that, no refund will be made.
  • After making the payment, the delegate can postpone the training date and join another batch, without any additional charges.

Soft Copy Certificate

Hard Copy Certificate

Green Belt Project Guidance

Black Belt Project Guidance

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Weekend Training Dates:

Sep 4, 10, 11

Sep 9, 10, 16

Oct 8, 9, 15

Oct 8, 14, 15

Nov 12, 13, 19

Nov 11, 12, 19

Dec 4, 10, 11

Dec 9, 10, 16

Weekdays Training Dates:

Sep 14, 15, 16, 19, 20, 21, 22, 23, 26, 27

Oct 12, 13, 14, 17, 18, 19, 20, 21, 27, 28

Nov 15, 16, 17, 18, 21, 22, 23, 24, 28, 29

Dec 14, 15, 16, 19, 20, 21, 22, 23, 26, 27

Weekend Training Dates:

Sep 17, Oct 1, 8, 15

Oct 22, 29, Nov 5, 12

Weekdays Training Dates:

Contact enquiry@anexas.net

Weekend Training Dates:

Sep 24, Oct 1, 8, 15, 22

Weekdays Training Dates:

Oct 17, 18, 19, 20, 27, 31, Nov 2, 3, 7, 8, 9, 10, 14, 15,16, 17

Weekend Training Dates:

Sep 24, 25, Oct 1

Sep 24, 30,  Oct 1

Oct 30, Nov 5, 6

Oct 29, Nov 4, 5

Nov 26, 27, Dec 3

Nov 26, Dec 2

Weekdays Training Dates:

Sep 28, 29, 30 Oct 3, 4, 6, 7, 10, 11

Oct 31, Nov 2, 3, 4, 7, 8, 9, 10, 11, 14

Nov 30, Dec 1, 2, 5, 6, 7, 8, 9, 12, 13

Soft Copy Certificate

Hard Copy Certificate