Berapa biaya belajar python untuk ilmu data?

Kursus Ilmu Data dengan Python ini memberi Anda gambaran lengkap tentang alat dan teknik analitik data Python. Learning python is a crucial skill for many data science roles, and you can develop it with this Python data science course. With a blended learning approach, you can learn Python for data science along with concepts like data wrangling, mathematical computing, and more. Unlock your career as a data scientist with Simplilearn’s Data Science with Python training

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Data Science with Python Course Overview

The Data Science with Python course teaches you to master the concepts of Python programming. Through this Python for Data Science training, you will learn Data Analysis, Machine Learning, Data Visualization, Web Scraping, & NLP. Upon course completion, you will master the essential Data Science tools using Python

Data Science with Python Training Key Features

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Di Simplilearn, kami sangat menghargai kepercayaan pelanggan kami. Namun, jika Anda merasa bahwa kursus Ilmu Data dengan Python ini tidak memenuhi harapan Anda, kami menawarkan jaminan uang kembali 7 hari. Kirimkan saja permintaan pengembalian dana kepada kami melalui email dalam waktu 7 hari setelah pembelian dan kami akan mengembalikan 100% pembayaran Anda, tanpa pertanyaan

• 68 jam pembelajaran campuran
• 4 proyek berbasis industri
• Pembelajaran interaktif dengan lab notebook Jupyter
• Akses seumur hidup ke pembelajaran mandiri
• Sesi mentoring khusus dari fakultas pakar industri

Keterampilan Tercakup

• Perselisihan data
• Eksplorasi data
• Visualisasi data
• Komputasi matematis
• Mengikis web
• Membangun hipotesis
• Konsep pemrograman Python
• Paket NumPy dan SciPy
• Paket Scikit Learn untuk Natural Language Processing

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Manfaat

Data Science is an evolving field and Python has become a required skill for 46-percent of jobs in Data Science. According to the US Bearue of Labor Statistics around 11. 6 million data science jobs will be created by 2026  and professionals with Python skills will have an additional advantage

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Training Options

Self-Paced Learning

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• 4 hands-on projects to perfect the skills learnt
• 3 simulation test papers for self-assessment
• Lab access to practice live during sessions
• 24x7 learner assistance and support

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Data Science with Python Course Curriculum

Eligibility

The demand for Data Science with Python programming professionals has surged, making this course well-suited for participants at all levels of experience. This Data Science with Python course is beneficial for analytics professionals willing to work with Python, Software, and IT professionals interested in the field of analytics, and anyone with a genuine interest in Data Science

Pre-requisites

Learners need to possess an undergraduate degree or a high school diploma. To best understand the Python Data Science course, it is recommended that you begin with the courses including, Introduction to Data Science in Python, Math Refresher, Data Science in Real Life, and Statistics Essentials for Data Science. These courses are offered as free companions with this training.  

Course Content

• Data Science with Python

  Preview

  • Lesson 01. Course Introduction

    08. 57Preview

    • 1. 01 Course Introduction

      05. 46

    • 1. 02 Demo. Jupyter Lab Walk - Through

      03. 11

  • Lesson 02. Introduction to Data Science

    09. 10Preview

    • 2. 01 Learning Objectives

      00. 27

    • 2. 02 Metodologi Ilmu Data

      01. 20

    • 2. 03 Dari Pemahaman Bisnis ke Pendekatan Analitik

      01. 02

    • 2. 04 Dari Persyaratan hingga Pengumpulan

      01. 06

    • 2. 05 Dari Pemahaman Hingga Persiapan

      01. 10

    • 2. 06 Dari Pemodelan hingga Evaluasi

      01. 53

    • 2. 07 Dari Penerapan hingga Umpan Balik

      01. 52

    • 2. 08 Pengambilan Kunci

      00. 20

  • Pelajaran 03. Perpustakaan Python untuk Ilmu Data

    01. 15. 08Pratinjau

    • 3. 01 Tujuan Pembelajaran

      00. 37

    • 3. 02 Perpustakaan Python untuk Ilmu Data

      01. 51

    • 3. 03 Impor Perpustakaan ke dalam Program Python

      01. 05

    • 3. 04 Numpy

      04. 35

    • 3. 05 Demo. Numpy

      05. 08

    • 3. 06 Panda

      04. 12

    • 3. Seri 07

      07. 57

    • 3. 08 Bingkai Data

      05. 37

    • 3. 09 Demo Panda. Bagian satu

      04. 51

    • 3. 10 Demo Panda. Bagian kedua

      02. 59

    • 3. 11 Matplotlib

      06. 04

    • 3. 12 Demo. Matplotlib

      02. 09

    • 3. 13 Sains

      05. 23

    • 3. 14 Demo. Pedas

      01. 38

    • 3. 15 Scikit belajar

      02. 18

    • 3. 16 Demo. Scikit belajar

      06. 20

    • 3. 17 Mengikis Web dengan Sup Cantik

      06. 23

    • 3. 18 Pengurai

      03. 07

    • 3. 19 Demo. Pengikisan Web dengan Sup Cantik

      02. 20

    • 3. 20 Pengambilan Kunci

      00. 34

  • Pelajaran 04. Perselisihan Data

    31. 32Pratinjau

    • 4. 01 Tujuan Pembelajaran

      00. 42

    • 4. 02 Eksplorasi Data Memuat File. Bagian A

      02. 53

    • 4. 03 Eksplorasi Data Memuat File. Bagian B

      01. 36

    • 4. 04 Teknik Eksplorasi Data. Bagian A

      02. 44

    • 4. 05 Teknik Eksplorasi Data. Bagian B

      02. 48

    • 4. 06 lahir laut

      02. 19

    • 4. 07 Demo. Analisis korelasi

      02. 38

    • 4. 08 Perselisihan Data

      01. 28

    • 4. 09 Nilai yang Hilang dalam Kumpulan Data

      01. 57

    • 4. 10 Nilai Outlier dalam Kumpulan Data

      01. 50

    • 4. 11 Demo. Perlakuan Outlier dan Missing Value

      04. 12

    • 4. 12 Manipulasi Data

      00. 49

    • 4. 13 Fungsi Objek Data dengan Python. Bagian A

      01. 50

    • 4. 14 Fungsi Objek Data dengan Python. Bagian B

      01. 34

    • 4. 15 Berbagai Jenis Gabungan

      01. 34

    • 4. 16 Takeaways Kunci

      00. 38

  • Pelajaran 05. Rekayasa Fitur

    07. 03Pratinjau

    • 5. 01 Tujuan Pembelajaran

      00. 28

    • 5. 02 Pengantar Rekayasa Fitur

      01. 50

    • 5. 03 Pengkodean Variabel Kategorikal

      00. 27

    • 5. 04 Pengkodean Label

      01. 46

    • 5. 05 Teknik yang digunakan untuk Encoding variabel

      02. 11

    • 5. 06 Pengambilan Kunci

      00. 21

  • Pelajaran 06. Analisis Data Eksplorasi

    24. 58Pratinjau

    • 6. 01 Tujuan Pembelajaran

      00. 33

    • 6. 02 Jenis Plot

      09. 38

    • 6. 03 Plot dan Subplot

      10. 06

    • 6. 04 Tugas 01. Demo plot berpasangan

      02. 28

    • 6. 05 Tugas 02. Demo Bagan Pai

      01. 52

    • 6. 06 Pengambilan Kunci

      00. 21

  • Pelajaran 07. Seleksi Fitur

    28. 08Pratinjau

    • 7. 01 Tujuan Pembelajaran

      00. 34

    • 7. 02 Pemilihan Fitur

      01. 28

    • 7. 03 Regresi

      00. 54

    • 7. 04 Analisis Faktor

      01. 58

    • 7. 05 Proses Analisis Faktor

      01. 07

    • 7. 06 Analisis Komponen Utama (PCA)

      02. 32

    • 7. 07 Komponen Utama Pertama

      02. 44

    • 7. 08 Nilai Eigen dan PCA

      02. 33

    • 7. 09 Demo. Pengurangan Fitur

      05. 36

    • 7. 10 Analisis Diskriminan Linear

      02. 28

    • 7. 11 Garis Terpisah Maksimum

      00. 45

    • 7. 12 Temukan Garis Terpisah Maksimum

      03. 12

    • 7. 13 Demo. Pengurangan Fitur Berlabel

      01. 55

    • 7. 14 Takeaways Kunci

      00. 22

  • Proyek Praktek

    • Pemodelan Atrisi Karyawan IBM HR Analytics

• Kursus Gratis

• Penyegaran Matematika

  Preview

  • Lesson 01. Course Introduction

    06. 23Pratinjau

    • 1. 01 Tentang Simplelearn

      00. 28

    • 1. 02 Pengantar Matematika

      01. 18

    • 1. 03 Jenis Matematika

      02. 39

    • 1. 04 Aplikasi Matematika dalam Industri Data

      01. 17

    • 1. 05 Jalur Pembelajaran

      00. 25

    • 1. 06 Komponen Kursus

      00. 16

  • Pelajaran 02. Probabilitas dan Statistik

    27. 27Pratinjau

    • 2. 01 Learning Objectives

      00. 29

    • 2. 02 Dasar-dasar Statistik dan Probabilitas

      03. 08

    • 2. 03 Pengantar Statistik Deskriptif

      02. 12

    • 2. 04 Ukuran Tendensi Sentral​

      04. 50

    • 2. 05 Ukuran Asimetri​

      01. 10

    • 2. 06 Ukuran Variabilitas​

      03. 49

    • 2. 07 Ukuran Hubungan​

      02. 31

    • 2. 08 Pengantar Probabilitas

      08. 36

    • 2. 09 Pengambilan Kunci

      00. 42

    • 2. 10 Pemeriksaan pengetahuan

  • Pelajaran 03. Koordinat geometri

    06. 31Pratinjau

    • 3. 01 Tujuan Pembelajaran

      00. 35

    • 3. 02 Pengantar Geometri Koordinat​

      03. 16

    • 3. 03 Rumus Geometri Koordinat​

      01. 51

    • 3. 04 Pengambilan Kunci

      00. 49

    • 3. 05 Pemeriksaan Pengetahuan

  • Pelajaran 04. Linear Algebra

    29. 53Pratinjau

    • 4. 01 Tujuan Pembelajaran

      00. 29

    • 4. 02 Pengantar Aljabar Linear

      03. 21

    • 4. 03 Bentuk Persamaan Linear

      05. 21

    • 4. 04 Memecahkan Persamaan Linear

      05. 21

    • 4. 05 Pengantar Matriks

      02. 05

    • 4. 06 Operasi Matriks

      07. 07

    • 4. 07 Pengantar Vektor

      01. 00

    • 4. 08 Jenis dan Properti Vektor

      01. 52

    • 4. 09 Operasi Vektor

      02. 39

    • 4. 10 Takeaways Kunci

      00. 38

    • 4. 11 Pemeriksaan Pengetahuan

  • Pelajaran 05. Nilai Eigen Vektor Eigen dan Komposisi Eigen

    08. 56Pratinjau

    • 5. 01 Tujuan Pembelajaran

      00. 29

    • 5. 02 Nilai Eigen

      01. 19

    • 5. 03 vektor Eigen

      04. 09

    • 5. 04 komposisi Eigende

      02. 21

    • 5. 05 Pengambilan Kunci

      00. 38

    • 5. 06 Knowledge Check

  • Lesson 06. Introduction to Calculus

    09. 47Preview

    • 6. 01 Tujuan Pembelajaran

      00. 30

    • 6. 02 Basics of Calculus

      01. 20

    • 6. 03 Differential Calculus

      03. 01

    • 6. 04 Differential Formulas

      01. 01

    • 6. 05 Kalkulus Integral

      02. 33

    • 6. 06 Rumus Integrasi

      00. 47

    • 6. 07 Pengambilan Kunci

      00. 35

    • 6. 08 Pemeriksaan Pengetahuan

• Kursus Gratis

• Statistics Essential for Data Science

  Preview

  • Lesson 01. Course Introduction

    07. 05Pratinjau

    • 1. 01 Course Introduction

      05. 19

    • 1. 02 Apa yang Akan Anda Pelajari

      01. 46

  • Pelajaran 02. Pengantar Statistik

    18. 40Pratinjau

    • 2. 01 Learning Objectives

      01. 16

    • 2. 02 Apa Itu Statistik

      01. 50

    • 2. 03 Mengapa Statistik

      02. 06

    • 2. 04 Perbedaan antara Populasi dan Sampel

      01. 20

    • 2. 05 Berbagai Jenis Statistik

      02. 42

    • 2. 06 Pentingnya Konsep Statistik dalam Ilmu Data

      03. 20

    • 2. 07 Penerapan Konsep Statistik Dalam Bisnis

      02. 11

    • 2. 08 Studi Kasus Penggunaan Statistik dalam Bisnis

      03. 09

    • 2. 09 Rekap

      00. 46

  • Pelajaran 03. Memahami Data

    17. 29Pratinjau

    • 3. 01 Tujuan Pembelajaran

      01. 12

    • 3. 02 Jenis Data dalam Konteks Bisnis

      02. 11

    • 3. 03 Kategorisasi Data dan Jenis Data

      03. 13

    • 3. 03 Jenis Pengumpulan Data

      02. 14

    • 3. 04 Jenis Data

      02. 01

    • 3. 05 Terstruktur vs. Data Tidak Terstruktur

      01. 46

    • 3. 06 Sumber Data

      02. 17

    • 3. 07 Masalah Kualitas Data

      01. 38

    • 3. 08 Rekap

      00. 57

  • Pelajaran 04. Statistik deskriptif

    32. 48Pratinjau

    • 4. 01 Tujuan Pembelajaran

      01. 26

    • 4. 02 Rata-Rata Matematika dan Posisi

      03. 15

    • 4. 03 Ukuran Tendensi Sentral. Bagian A

      02. 17

    • 4. 04 Ukuran Tendensi Sentral. Bagian B

      02. 41

    • 4. 05 Ukuran Dispersi

      01. 15

    • 4. 06 Penyimpangan Kuartil Range Outlier

      02. 30

    • 4. 07 Mean Absolute Deviation (MAD) Standard Deviation Varians

      03. 37

    • 4. 08 Skor Z dan Aturan Empiris

      02. 14

    • 4. 09 Koefisien Variasi dan Penerapannya

      02. 06

    • 4. 10 Ukuran Bentuk

      02. 39

    • 4. 11 Meringkas Data

      02. 03

    • 4. 12 Rekap

      00. 54

    • 4. 13 Studi Kasus Satu. Statistik deskriptif

      05. 51

  • Pelajaran 05. Visualisasi data

    20. 55Pratinjau

    • 5. 01 Tujuan Pembelajaran

      00. 57

    • 5. 02 Visualisasi Data

      02. 15

    • 5. 03 Bagan Dasar

      01. 52

    • 5. 04 Bagan Tingkat Lanjut

      02. 19

    • 5. 05 Interpretasi Grafik

      02. 57

    • 5. 06 Memilih Bagan yang Sesuai

      02. 25

    • 5. 07 Bagan Lakukan dan Jangan Lakukan

      02. 47

    • 5. 08 Bercerita Dengan Bagan

      01. 29

    • 5. 09 Rekap

      00. 50

    • 5. 10 Studi Kasus Dua. Visualisasi data

      03. 04

  • Pelajaran 06. Kemungkinan

    19. 49Pratinjau

    • 6. 01 Tujuan Pembelajaran

      00. 55

    • 6. 02 Pengantar Probabilitas

      03. 10

    • 6. 03 Istilah Kunci dalam Probabilitas

      02. 25

    • 6. 04 Probabilitas Bersyarat

      02. 11

    • 6. 05 Jenis Acara. Mandiri dan Tergantung

      02. 59

    • 6. 06 Penambahan Teorema Probabilitas

      01. 58

    • 6. 07 Teorema Probabilitas Perkalian

      02. 08

    • 6. 08 Teorema Bayes

      03. 10

    • 6. 09 Rekap

      00. 53

  • Pelajaran 07. Distribusi Probabilitas

    23. 20Pratinjau

    • 7. 01 Tujuan Pembelajaran

      00. 52

    • 7. 02 Variabel Acak

      02. 21

    • 7. 03 Distribusi Probabilitas Diskrit vs. Kontinu. Bagian A

      01. 44

    • 7. 04 Distribusi Probabilitas Diskrit vs. Kontinu. Bagian B

      01. 45

    • 7. 05 Distribusi Probabilitas Diskrit yang Umum Digunakan. Bagian A

      03. 18

    • 7. 06 Distribusi Probabilitas Diskrit. Poisson

      03. 16

    • 7. 07 Binomial dengan Teorema Poisson

      02. 28

    • 7. 08 Distribusi Probabilitas Berkelanjutan yang Umum Digunakan

      03. 22

    • 7. 09 Penerapan Distribusi Normal

      02. 49

    • 7. 10 Rekap

      01. 25

  • Pelajaran 08. Teknik Pengambilan Sampel dan Pengambilan Sampel

    30. 53Pratinjau

    • 8. 01 Tujuan Pembelajaran

      00. 51

    • 8. 02 Pengantar Sampling dan Kesalahan Sampling

      03. 05

    • 8. 03 Kelebihan dan Kekurangan Pengambilan Sampel

      01. 31

    • 8. 04 Metode Sampling Probabilitas. Bagian A

      02. 32

    • 8. 05 Metode Sampling Probabilitas. Bagian B

      02. 27

    • 8. 06 Metode Sampling Non-Probabilitas. Bagian A

      01. 42

    • 8. 07 Metode Sampling Non-Probabilitas. Bagian B

      01. 25

    • 8. 08 Penggunaan Probability Sampling dan Non-Probability Sampling

      02. 08

    • 8. 09 Pengambilan sampel

      01. 08

    • 8. 10 Distribusi Probabilitas

      02. 53

    • 8. 11 Teorema Lima Titik Satu

      00. 52

    • 8. 12 Teorema Limit Pusat

      02. 14

    • 8. 13 Rekap

      01. 07

    • 8. 14 Studi Kasus Tiga. Sampel dan Teknik Pengambilan Sampel

      05. 16

    • 8. 15 Sorotan

      01. 42

  • Pelajaran 09. Statistik Inferensial

    33. 59Pratinjau

    • 9. 01 Tujuan Pembelajaran

      01. 04

    • 9. 02 Hipotesis dan Pengujian Hipotesis dalam Bisnis

      03. 24

    • 9. 03 Hipotesis Null dan Alternatif

      01. 44

    • 9. 04 Nilai P

      03. 22

    • 9. 05 Tingkat Signifikansi

      01. 16

    • 9. 06 Ketik Kesalahan Satu dan Dua

      01. 37

    • 9. 07 Uji Z

      02. 24

    • 9. 08 Interval Keyakinan dan Tingkat Signifikansi Persentase. Bagian A

      02. 52

    • 9. 09 Interval Keyakinan. Bagian B

      01. 20

    • 9. 10 Tes Satu Ekor dan Dua Ekor

      04. 43

    • 9. 11 Catatan untuk Diingat untuk Hipotesis Nol

      01. 02

    • 9. 12 Hipotesis Alternatif

      01. 51

    • 9. 13 Rekap

      00. 56

    • 9. 14 Studi Kasus 4. Statistik Inferensial

      06. 24

    • Pengujian Hipotesis

  • Pelajaran 10. Penerapan Statistik Inferensial

    27. 20Pratinjau

    • 10. 01 Tujuan Pembelajaran

      00. 50

    • 10. 02 Analisis Bivariat

      02. 01

    • 10. 03 Memilih Tes yang Sesuai untuk EDA

      02. 29

    • 10. 04 Parametrik vs. Tes Non-Parametrik

      01. 54

    • 10. 05 Uji Signifikansi

      01. 38

    • 10. 06 Uji Z

      04. 27

    • 10. 07 Tes T

      00. 54

    • 10. 08 Tes Parametrik ANOVA

      03. 26

    • 10. 09 Uji Chi-Square

      02. 31

    • 10. 10 Tes Tanda

      01. 58

    • 10. 11 Tes Kruskal Wallis

      01. 04

    • 10. 12 Uji Mann Whitney Wilcoxon

      01. 18

    • 10. 13 Uji Jalankan untuk Keacakan

      01. 53

    • 10. 14 Rekap

      00. 57

  • Pelajaran 11. Hubungan antar Variabel

    18. 08Pratinjau

    • 11. 01 Tujuan Pembelajaran

      01. 06

    • 11. 02 Korelasi

      01. 54

    • 11. 03 Koefisien Korelasi Karl Pearson

      02. 36

    • 11. 04 Karl Pearsons. Gunakan Kasus

      01. 30

    • 11. 05 Koefisien Korelasi Peringkat Spearman

      02. 14

    • 11. 06 Penyebab

      01. 47

    • 11. 07 Contoh Regresi

      02. 28

    • 11. 08 Koefisien Determinasi

      01. 12

    • 11. 09 Mengukur Kualitas

      02. 29

    • 11. 10 Rekap

      00. 52

  • Pelajaran 12. Penerapan Statistik dalam Bisnis

    17. 25Pratinjau

    • 12. 01 Tujuan Pembelajaran

      00. 53

    • 12. 02 Cara Menggunakan Statistik Dalam Bisnis Sehari-Hari

      03. 29

    • 12. 03 Contoh. Bagaimana Tidak Berbohong Dengan Statistik

      02. 34

    • 12. 04 Bagaimana Tidak Berbohong Dengan Statistik

      01. 49

    • 12. 05 Berbohong Melalui Visualisasi

      02. 15

    • 12. 06 Lying About Relationships

      03. 31

    • 12. 07 Recap

      01. 06

    • 12. 08 Spotlight

      01. 48

  • Lesson 13. Assisted Practice

    11. 47Preview

    • Assisted Practice. Problem Statement

      02. 10

    • Assisted Practice. Solution

      09. 37

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Data Science with Python Exam & Certification

• Who provides the certification and how long is it valid for?

  Once you successfully complete the Data Science with Python training, Simplilearn will provide you with an industry-recognized course completion certificate which will have a lifelong validity

• What do I need to unlock my Simplilearn certificate?

  Kelas Daring

  • Attend one complete batch of Data Science with Python training
  • Submit at least one completed project

  Online Self-Learning

  • Complete 85% of the course
  • Submit at least one completed project

• Do you provide any practice tests as part of Data Science with Python course?

  Yes, we provide 1 practice test as part of our Data Science with Python course to help you prepare for the actual certification exam. You can try this Free Data Science with Python Practice Test to understand the type of tests that are part of the course curriculum.   

Data Science with Python Course Reviews

• Vignesh Manikandan

  The online classes were well-paced and helped us learn a ton of stuff within a short amount of time. Vaishali is very knowledgeable and handled all the sessions with outstanding professionalism. Thanks for your expertise

• Arvind Kumar

  Technology Lead

  It was a great learning experience. My trainer, Vaishali delivered each session well. All topics were explained with in-depth theory, real-time examples, and execution of the same in Python. Her teaching methodology enhanced the learning process

• Mushtaque Ansari

  Senior Software Developer

  I had a wonderful experience learning Data Science with Python with Simplilearn. Thank you, Vaishali for explaining concepts theoretically and practically. The live sessions helped me easily understand the concepts

• Brian

  Program Manager (iGPM RBEI)

  The training was well-structured, and the trainer was experienced with hands-on know-how. The trainer handled responses and queries efficiently with a good amount of patience

• Darshan Gajjar

  I learned a lot about Python, Numpy, Pandas, Visualization. The instructor, Swagat was excellent in explaining things clearly. The support team is also accommodative and resolves issues instantly

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  Aashish Kumar

  I completed this course at Simplilearn. The faculty, Prashanth Nair, was extremely knowledgeable, and the entire class appreciated his way of teaching. Simplilearn's support team was very accommodating and quick in providing responses. I was able to grab a 30% hike in my salary after getting certified

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  C Muthu Raman

  Simplilearn facilitates a brilliant platform to acquire new & relevant skills at ease. Well laid out course content and expert faculty ensure an excellent learning experience

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  Incredible mentorship, and amazing and unique lectures. Simplilearn provides a great way to learn with self-paced videos and recordings of online sessions. Thanks, Simplilearn, for providing quality education

• 

  Mukesh Pandey

  Simplilearn is an excellent platform for online learning. Their course curriculum is comprehensive and up to date. Kami mendapatkan akses seumur hidup ke sesi yang direkam jika kami perlu menyegarkan kembali pemahaman kami. Jika Anda ingin meningkatkan keterampilan, saya sarankan Anda mendaftar dengan Simplilearn. Mereka menawarkan kelas di hampir semua disiplin ilmu

• 

  Surendaran Baskaran

  Saya mengikuti kursus ini dengan Simplelearn. Instruktur berpengetahuan luas dan berbagi keterampilan dan pengetahuan mereka. Pengalaman belajar saya sangat luar biasa dengan Simplilearn. Laboratorium praktik dan materi sangat membantu untuk pembelajaran yang lebih baik. Terima kasih, Simplelearn. Selamat Belajar

• 

  Shiv Sharma

  Prashant Nair adalah fakultas yang luar biasa. Cara dia menyederhanakan, menghubungkan, dan menjelaskan topik sangat luar biasa. Saya ingin sekali mendaftar dan menghadiri semua kelasnya

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  Akash Raj

  Insinyur Teknologi

  The instructor not only delivers the lecture but also focuses on practical aspects related to the subject. This is something about the course that really impressed me

• 

  Kiran Kumar

  I recently enrolled in the Data Scientist Master’s Program at Simplilearn. The syllabus is systematically structured, and the Live sessions are explained with real-time examples. This makes the course more accessible to freshers with basic knowledge. Looking forward to completing it. Thanks, Simplilearn Team

• 

  Satabdi Adhikary

  Simplilearn's courses are affordable and helped me learn something new during the lockdown. Moreover, I also got to add a Well-Known Global Name like Simplilearn to my resume. I could choose the trainer as well as enroll for multiple sessions using the Flexible Pass

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Why Online Bootcamp

• Develop skills for real career growthCutting-edge curriculum designed in guidance with industry and academia to develop job-ready skills
• Learn from experts active in their field, not out-of-touch trainersLeading practitioners who bring current best practices and case studies to sessions that fit into your work schedule
• Learn by working on real-world problemsCapstone projects involving real world data sets with virtual labs for hands-on learning
• Structured guidance ensuring learning never stops24x7 Learning support from mentors and a community of like-minded peers to resolve any conceptual doubts

Data Science with Python Training FAQs

• Why learn Python for Data Science?

  Python is the most popular programming language for Data Science. Python is widely used to perform data analysis, data manipulation, and data visualization. The advantages of using Python for data science are

  • Python offers access to a wide variety of Data Science libraries and it is the ideal language for implementing algorithms and the rapid development of applications in Data Science
  • Python is an object-oriented programming language with integrated dynamic semantics, used primarily for application and web development. The widely used language offers dynamic binding and dynamic typing options
  • Python is a high-level programming language with an enormous community. Its flexibility is quite useful for any issues related to application development in Data Science

• Can I learn Python Data Science course online?

  The rapid evolution of learning methodologies, thanks to the influx of technology, has increased the ease and efficiency of online learning, making it possible to learn at your own pace. Simplilearn's Python Data Science course provides live classes and access to study materials from anywhere and at any time. Our extensive (and growing) collection of blogs, tutorials, and YouTube videos will help you get up to speed on the main concepts. Even after your class ends, we provide a 24/7 support system to help you with any questions or concerns you may have

• What is the job outlook for Data Science with Python programming professionals?

  Harvard Business Review has already named Data Scientist as the ‘Sexiest Job of the 21st Century. ’ The statement is echoed in LinkedIn Emerging Jobs Report 2021 in which Data Science specialists are one of the top emerging jobs in the US with Python as one of its key skills. The job role has witnessed an annual growth of 35 percent for Data scientists and Data engineers

• Do I need coding experience to learn Python for Data Science?

  If you have prior coding experience or familiarity with any other object-oriented programming language, it will be easier for you to learn Python for Data Science. However, it is not compulsory

• I have familiarity in other programming languages like C++/Java. Will the Data Science with Python course help me to switch to Python?

  Python has simple syntax and is easy to understand. Knowledge of Java or C++ language helps in learning Python faster. This is because Python is also object-oriented and many of its prototypes are similar to Java. So you can easily migrate to Python with this comprehensive course

• How much Python is required for Data Science?

  Python digunakan untuk berbagai aplikasi dan Anda tidak perlu terbiasa dengan semua pustaka dan modulnya. Even if you know the basics of Python, this Data Science with Python certification covers the popular libraries of Python that are used in data science projects

• Does Python support any open-source libraries?

  Yes, Python supports a lot of open-source libraries like SciPy, NumPy, Scikit-Learn, TensorFlow, Matplotlib, and Pandas

• Does the knowledge imparted through this Data Science with Python certification apply to Machine Learning and Data Science projects?

  Yes, our Data Science with Python course is specifically designed to impart industry-oriented skills. The course material, practice with integrated labs, and real-world projects enhance your practical knowledge and help you apply them to Data Science projects

• How can I get started with this Data Science with Python course?

  It is beneficial if you brush up your skills in core math, statistics, and programming basics to get started with this Data Science with Python course

• Which companies use Python for Data Science?

  Major companies like Google, Instagram, Goldman Sachs, Facebook, Quora, Netflix, Dropbox, and PayPal use Python for Data Science

• How do Data Scientists use Python in daily work?

  Data scientists handle a variety of tasks in their day-to-day routine. They gather, merge, and analyze data and identify trends and patterns. They also build and test new algorithms to simplify data problems. Python is used along with other tools to perform all these tasks

• What are the system requirements to install Python for Data Science?

  To run Python, your system must fulfill the following basic requirements

  • 32 or 64-bit Operating System
  • 1GB RAM 

  The instruction uses Anaconda and Jupyter notebooks. The e-learning videos provide detailed instructions on how to install them

• Which is better for Data Science — R or Python?

  Python and R are both popular languages among data scientists. While R is a statistical analysis language, Python is a general-purpose language that has a readable syntax and well-structured code. Data professionals prefer Python for its versatility and R for its better visualization capabilities. However, deciding on the best-suited programming language depends on the nature of the data analysis task you are working on

• What will I learn in the Python for Data Science course?

  When learning about Data Science with Python, you will gain a clear understanding of Python topics like functions, classes, lists, dictionaries, sets, tuples, and various Python libraries. Further, you will go through concepts like mathematical computing, data visualization, data exploration, data analysis, web scraping, machine learning, and feature engineering

• What are the must-have Python packages for Data Science?

  Some of the widely used Python libraries for data science include TensorFlow, NumPy, Keras, Matplotlib, scikit-learn, PyTorch, Scrapy, SciPy, and Pandas

• Are OOPs in Python necessary for a Data Science career?

  No, it is not mandatory to learn OOPs in Python when starting a career in Data Science. However, knowledge of OOP basics is beneficial when performing daily Data Science tasks

• Who are our instructors and how are they selected?

  All of our highly qualified Data Science trainers are industry experts with at least 10-12 years of relevant teaching experience. Each of them has gone through a rigorous selection process that includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating remain on our faculty

• What are the modes of training offered for this Python Data Science course?

  Live Virtual Classroom or Online Classroom. In online classroom training, you have the convenience of attending the Python Data Science course remotely from your desktop via video conferencing to enhance your productivity and reduce the time spent away from work or home
   
  Online Self-Learning. In this mode, you will receive lecture videos and can proceed through the course at your convenience
   
  WinPython portable distribution is the open-source environment on which all hands-on exercises will be performed. Instructions for installation will be given during the training

• Is this live training, or will I watch pre-recorded videos?

  If you enroll in the self-paced e-learning training program, you will have access to pre-recorded videos. However, if you enroll for the Online Classroom Flexi-Pass, you will have access to both instructor-led Data Science with Python training conducted online as well as the pre-recorded videos

• What if I miss a class?

  Simplilearn provides recordings of each class so you can review them as needed before the next session

• Can I cancel my enrollment? Will I get a refund?

  Yes, you can cancel your enrollment if necessary. We will refund the course price after deducting an administration fee. To learn more, you can view our Refund Policy

• Are there any group discounts for classroom training programs?

  Ya, kami memiliki paket diskon kelompok untuk program pelatihan kelas. Contact Help & Support to learn more about group discounts

• Bagaimana cara saya mendaftar untuk kursus Ilmu Data Python?

  You can enroll for this Data Science with Python certification training on our website and make an online payment using any of the following options.  

  • Kartu Kredit atau Debit Visa
  • MasterCard
  • American Express
  • Diner’s Club
  • PayPal 

  Once payment is received you will automatically receive a payment receipt and access information via email

• Whom should I contact to learn more about this Python Data Science course?

  Contact us using the form on the right of any page on the Simplilearn website, or select the Live Chat link. Our customer service representatives can provide you with more details

• What is Global Teaching Assistance?

  Our teaching assistants are a dedicated team of subject matter experts here to help you get certified in Data Science on your first attempt. They engage students proactively to ensure the course path is being followed and help you enrich your learning experience, from class onboarding to project mentoring and job assistance. Teaching Assistance is available during business hours

• What is covered under the 24/7 Support promise?

  We offer 24/7 support through email, chat, and calls. We also have a dedicated team that provides on-demand assistance through our community forum. What’s more, you will have lifetime access to the community forum, even after completion of your Python Data Science course with us

• What is the recommended learning path after completing Data Science with Python course?

  You can either enroll in our Data Scientist Course or if you are looking to get a University certificate, you can enroll in the Professional Certificate Program in Data Science

• Disclaimer

  The projects have been built leveraging real publicly available data-sets of the mentioned organizations

• How do I become a Data Science Expert?

  To become a data science expert, all you need is prior experience in mathematics or statistics and knowledge of programming languages like Python, Java, C++, etc. Simplilearn helps you gain expertise in Data Science with its Data Science with Python certification and have a successful career

• What is Data Science used for?

  Data science collects relevant data, analyzes and interprets, and finds solutions for addressing business problems. Starting from healthcare to advertising, Data Science has applications in almost every possible field

• Is a Data Science with Python course difficult to learn?

  Not at all. Simplilearn’s Data Science with Python course has been tailored to meet the learning objectives of both beginners and experienced people and can be easily pursued by anyone meeting the course eligibility requirements

• Is Data Science a good career option?

  Yes, Data Science is definitely a good career option given the following reasons

  • Data science is everywhere and expanding at an exponential rate. The market size of Data science has been projected to reach $178 billion by the end of 2025
  • As highlighted by the US Bureau of Labour Statistics (BLS), job roles requiring Data Science-related skills will likely surge by 2026
  • Data Scientists are among the highest-paid professionals earning an average salary of $1,49,982 per year

• How do beginners learn Data Science with Python?

  While seeking data science with python training, beginners can first start with basics by completing the following fundamental modules included in the course

  • Python Basics
  • Penyegaran Matematika
  • Data Science in Real Life
  • Statistics Essentials for Data Science

  Upon developing a profound base in Data Science with Python, you can start with the course in the given order for a systematic learning experience

• Is Data Science with Python certification worth it?

  Yes, seeking data science with python training is worth it because, with the help of this certification, you’ll be able to

  • Attain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building and testing, and the basics of statistics
  • Memahami konsep penting pemrograman Python seperti tipe data, daftar, tupel, dikte, operator tingkat dasar, dan fungsi
  • Perform advanced level mathematical calculations utilizing the NumPy and SciPy packages, and their large library of mathematical functions
  • Carry analysis of data and manipulation using data structures and Pandas package tools
  • Gain an in-depth understanding of supervised and unsupervised learning models, such as logistic regression, linear regression, data clustering, dimension reduction, K-NN, and pipeline
  • Use the Scikit-Learn package for NLP and matplotlib library of Python for data visualization

• What are the job roles available after obtaining a Data Science with Python certification?

  After getting a data science with python certification, you can work as a

  • Business Analyst
  • Database Administrator
  • Big Data Engineer or Data Architect
  • Data Analyst
  • ML Engineer
  • Business Intelligence (BI) Developer
  • Business Intelligence Analyst
  • Statistician
  • Data Scientist
  • Computer Vision(CV) Engineer
  • Natural Language Processing (NLP) Engineer
  • MLOps Engineer

• What does a Data Science Expert do?

  A data science expert is primarily involved in collecting and analyzing data by utilizing various analytics and reporting tools to identify patterns, trends, and correlations in data sets. With the help of Simplilearn’s Data Science with Python certification, you will be able to gain a complete understanding of key roles and responsibilities of data science experts

• What skills should a Data Science Expert know?

  A data science expert should possess the following skills

  • Knowledge of programming languages like Python, R, and SQL
  • Profound knowledge of statistics and related concepts
  • Machine learning for handling big sets of data
  • Knowledge of Multivariable Calculus & Linear Algebra
  • Data wrangling to refine data
  • Knowledge of data visualization tools for easy communication of insights collected

  Seeking data science with python certification will help you gain all the skills mentioned above and have a flourishing career in data science

• What industries use Data Science most?

  Data Science has applications in every possible industry; however, some industries use data science extensively, such as retail, healthcare, banking and finance, construction, transportation, communications, media, and entertainment, education, manufacturing, natural resources, and energy and utility. Upon completing Simplilearn’s data science with python course, which is highly career-oriented, you can easily find job opportunities in these industries

• Which companies hire Data Science Experts?

  Some of the top recruiters hiring professionals with data science with Python certification are HData Systems, Hyperlink InfoSystem, Tata Consultancy Services, Accenture, Tech Mahindra, Capgemini India Pvt Ltd, Tiger Analytics, Genpact, LatentView Analytics, and DataFactz

• Which books do you suggest reading for Data Science with Python?

  To have a comprehensive data science with python training, you can consider referring to the following books

  • Python For Data Analysis written by Wes McKinney
  • Automate The Boring Stuff With Python written by Al Sweigart
  • Machine Learning with Python Cookbook written by Chris Albon
  • Python Cookbook written by Brian K. Jones and David M. Beazley
  • Hands-On Machine Learning with Scikit-Learn and TensorFlow written by Aurelien Geron
  • Data Visualization in Python by Gilbert Tanner

• What is the pay scale of Data Science professionals across the world?

  On average, professionals with Data Science with Python certification earn an annual salary of $97853

  How much Python should I learn for data science?

  While everyone is different, we've found that it takes three months to a year of consistent practice to learn Python for data science.

  How much will it cost to learn Python?

  Students will learn about the features and use of the python programming language in different applications. The course fee is Rs. 475 which has to be paid online.

  Berapa hari yang dibutuhkan untuk mempelajari Python untuk ilmu data?

  In general, it takes around two to six months to learn the fundamentals of Python. But you can learn enough to write your first short program in a matter of minutes. Developing mastery of Python's vast array of libraries can take months or years.

  Is Python hard to learn for data science?

  Python is the most popular and widely used programming language among Data Scientists. One of the main reasons for Python's popularity in the Data Science community is because of its ease of use and simplified syntax which makes it easy to learn and adapt for people having no engineering background.