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

• Designation
• Annual Salary
• Hiring Companies

• Annual Salary

  $43KMin

  $62KAverage

  $95KMax

  Source. Pintu kaca

  Hiring Companies

  Source. Indeed

• Annual Salary

  $83KMin

  $113KAverage

  $154KMax

  Source. Pintu kaca

  Hiring Companies

  Source. Indeed

Training Options

Self-Paced Learning

$ 899

• Lifetime access to high-quality self-paced eLearning content curated by industry experts
• 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 Introduction05. 46
    • 1. 02 Demo. Jupyter Lab Walk - Through03. 11

  • Lesson 02. Introduction to Data Science

    09. 10Preview
    • 2. 01 Learning Objectives00. 27
    • 2. 02 Metodologi Ilmu Data01. 20
    • 2. 03 Dari Pemahaman Bisnis ke Pendekatan Analitik01. 02
    • 2. 04 Dari Persyaratan hingga Pengumpulan01. 06
    • 2. 05 Dari Pemahaman Hingga Persiapan01. 10
    • 2. 06 Dari Pemodelan hingga Evaluasi01. 53
    • 2. 07 Dari Penerapan hingga Umpan Balik01. 52
    • 2. 08 Pengambilan Kunci00. 20

  • Pelajaran 03. Perpustakaan Python untuk Ilmu Data

    01. 15. 08Pratinjau
    • 3. 01 Tujuan Pembelajaran00. 37
    • 3. 02 Perpustakaan Python untuk Ilmu Data01. 51
    • 3. 03 Impor Perpustakaan ke dalam Program Python01. 05
    • 3. 04 Numpy04. 35
    • 3. 05 Demo. Numpy05. 08
    • 3. 06 Panda04. 12
    • 3. Seri 0707. 57
    • 3. 08 Bingkai Data05. 37
    • 3. 09 Demo Panda. Bagian satu04. 51
    • 3. 10 Demo Panda. Bagian kedua02. 59
    • 3. 11 Matplotlib06. 04
    • 3. 12 Demo. Matplotlib02. 09
    • 3. 13 Sains05. 23
    • 3. 14 Demo. Pedas01. 38
    • 3. 15 Scikit belajar02. 18
    • 3. 16 Demo. Scikit belajar06. 20
    • 3. 17 Mengikis Web dengan Sup Cantik06. 23
    • 3. 18 Pengurai03. 07
    • 3. 19 Demo. Pengikisan Web dengan Sup Cantik02. 20
    • 3. 20 Pengambilan Kunci00. 34

  • Pelajaran 04. Perselisihan Data

    31. 32Pratinjau
    • 4. 01 Tujuan Pembelajaran00. 42
    • 4. 02 Eksplorasi Data Memuat File. Bagian A02. 53
    • 4. 03 Eksplorasi Data Memuat File. Bagian B01. 36
    • 4. 04 Teknik Eksplorasi Data. Bagian A02. 44
    • 4. 05 Teknik Eksplorasi Data. Bagian B02. 48
    • 4. 06 lahir laut02. 19
    • 4. 07 Demo. Analisis korelasi02. 38
    • 4. 08 Perselisihan Data01. 28
    • 4. 09 Nilai yang Hilang dalam Kumpulan Data01. 57
    • 4. 10 Nilai Outlier dalam Kumpulan Data01. 50
    • 4. 11 Demo. Perlakuan Outlier dan Missing Value04. 12
    • 4. 12 Manipulasi Data00. 49
    • 4. 13 Fungsi Objek Data dengan Python. Bagian A01. 50
    • 4. 14 Fungsi Objek Data dengan Python. Bagian B01. 34
    • 4. 15 Berbagai Jenis Gabungan01. 34
    • 4. 16 Takeaways Kunci00. 38

  • Pelajaran 05. Rekayasa Fitur

    07. 03Pratinjau
    • 5. 01 Tujuan Pembelajaran00. 28
    • 5. 02 Pengantar Rekayasa Fitur01. 50
    • 5. 03 Pengkodean Variabel Kategorikal00. 27
    • 5. 04 Pengkodean Label01. 46
    • 5. 05 Teknik yang digunakan untuk Encoding variabel02. 11
    • 5. 06 Pengambilan Kunci00. 21

  • Pelajaran 06. Analisis Data Eksplorasi

    24. 58Pratinjau
    • 6. 01 Tujuan Pembelajaran00. 33
    • 6. 02 Jenis Plot09. 38
    • 6. 03 Plot dan Subplot10. 06
    • 6. 04 Tugas 01. Demo plot berpasangan02. 28
    • 6. 05 Tugas 02. Demo Bagan Pai01. 52
    • 6. 06 Pengambilan Kunci00. 21

  • Pelajaran 07. Seleksi Fitur

    28. 08Pratinjau
    • 7. 01 Tujuan Pembelajaran00. 34
    • 7. 02 Pemilihan Fitur01. 28
    • 7. 03 Regresi00. 54
    • 7. 04 Analisis Faktor01. 58
    • 7. 05 Proses Analisis Faktor01. 07
    • 7. 06 Analisis Komponen Utama (PCA)02. 32
    • 7. 07 Komponen Utama Pertama02. 44
    • 7. 08 Nilai Eigen dan PCA02. 33
    • 7. 09 Demo. Pengurangan Fitur05. 36
    • 7. 10 Analisis Diskriminan Linear02. 28
    • 7. 11 Garis Terpisah Maksimum00. 45
    • 7. 12 Temukan Garis Terpisah Maksimum03. 12
    • 7. 13 Demo. Pengurangan Fitur Berlabel01. 55
    • 7. 14 Takeaways Kunci00. 22

  • Proyek Praktek

    • Pemodelan Atrisi Karyawan IBM HR Analytics

• Kursus Gratis

• Penyegaran Matematika

  Preview

  • Lesson 01. Course Introduction

    06. 23Pratinjau
    • 1. 01 Tentang Simplelearn00. 28
    • 1. 02 Pengantar Matematika01. 18
    • 1. 03 Jenis Matematika02. 39
    • 1. 04 Aplikasi Matematika dalam Industri Data01. 17
    • 1. 05 Jalur Pembelajaran00. 25
    • 1. 06 Komponen Kursus00. 16

  • Pelajaran 02. Probabilitas dan Statistik

    27. 27Pratinjau
    • 2. 01 Learning Objectives00. 29
    • 2. 02 Dasar-dasar Statistik dan Probabilitas03. 08
    • 2. 03 Pengantar Statistik Deskriptif02. 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 Probabilitas08. 36
    • 2. 09 Pengambilan Kunci00. 42
    • 2. 10 Pemeriksaan pengetahuan

  • Pelajaran 03. Koordinat geometri

    06. 31Pratinjau
    • 3. 01 Tujuan Pembelajaran00. 35
    • 3. 02 Pengantar Geometri Koordinat​03. 16
    • 3. 03 Rumus Geometri Koordinat​01. 51
    • 3. 04 Pengambilan Kunci00. 49
    • 3. 05 Pemeriksaan Pengetahuan

  • Pelajaran 04. Linear Algebra

    29. 53Pratinjau
    • 4. 01 Tujuan Pembelajaran00. 29
    • 4. 02 Pengantar Aljabar Linear03. 21
    • 4. 03 Bentuk Persamaan Linear05. 21
    • 4. 04 Memecahkan Persamaan Linear05. 21
    • 4. 05 Pengantar Matriks02. 05
    • 4. 06 Operasi Matriks07. 07
    • 4. 07 Pengantar Vektor01. 00
    • 4. 08 Jenis dan Properti Vektor01. 52
    • 4. 09 Operasi Vektor02. 39
    • 4. 10 Takeaways Kunci00. 38
    • 4. 11 Pemeriksaan Pengetahuan

  • Pelajaran 05. Nilai Eigen Vektor Eigen dan Komposisi Eigen

    08. 56Pratinjau
    • 5. 01 Tujuan Pembelajaran00. 29
    • 5. 02 Nilai Eigen01. 19
    • 5. 03 vektor Eigen04. 09
    • 5. 04 komposisi Eigende02. 21
    • 5. 05 Pengambilan Kunci00. 38
    • 5. 06 Knowledge Check

  • Lesson 06. Introduction to Calculus

    09. 47Preview
    • 6. 01 Tujuan Pembelajaran00. 30
    • 6. 02 Basics of Calculus01. 20
    • 6. 03 Differential Calculus03. 01
    • 6. 04 Differential Formulas01. 01
    • 6. 05 Kalkulus Integral02. 33
    • 6. 06 Rumus Integrasi00. 47
    • 6. 07 Pengambilan Kunci00. 35
    • 6. 08 Pemeriksaan Pengetahuan

• Kursus Gratis

• Statistics Essential for Data Science

  Preview

  • Lesson 01. Course Introduction

    07. 05Pratinjau
    • 1. 01 Course Introduction05. 19
    • 1. 02 Apa yang Akan Anda Pelajari01. 46

  • Pelajaran 02. Pengantar Statistik

    18. 40Pratinjau
    • 2. 01 Learning Objectives01. 16
    • 2. 02 Apa Itu Statistik01. 50
    • 2. 03 Mengapa Statistik02. 06
    • 2. 04 Perbedaan antara Populasi dan Sampel01. 20
    • 2. 05 Berbagai Jenis Statistik02. 42
    • 2. 06 Pentingnya Konsep Statistik dalam Ilmu Data03. 20
    • 2. 07 Penerapan Konsep Statistik Dalam Bisnis02. 11
    • 2. 08 Studi Kasus Penggunaan Statistik dalam Bisnis03. 09
    • 2. 09 Rekap00. 46

  • Pelajaran 03. Memahami Data

    17. 29Pratinjau
    • 3. 01 Tujuan Pembelajaran01. 12
    • 3. 02 Jenis Data dalam Konteks Bisnis02. 11
    • 3. 03 Kategorisasi Data dan Jenis Data03. 13
    • 3. 03 Jenis Pengumpulan Data02. 14
    • 3. 04 Jenis Data02. 01
    • 3. 05 Terstruktur vs. Data Tidak Terstruktur01. 46
    • 3. 06 Sumber Data02. 17
    • 3. 07 Masalah Kualitas Data01. 38
    • 3. 08 Rekap00. 57

  • Pelajaran 04. Statistik deskriptif

    32. 48Pratinjau
    • 4. 01 Tujuan Pembelajaran01. 26
    • 4. 02 Rata-Rata Matematika dan Posisi03. 15
    • 4. 03 Ukuran Tendensi Sentral. Bagian A02. 17
    • 4. 04 Ukuran Tendensi Sentral. Bagian B02. 41
    • 4. 05 Ukuran Dispersi01. 15
    • 4. 06 Penyimpangan Kuartil Range Outlier02. 30
    • 4. 07 Mean Absolute Deviation (MAD) Standard Deviation Varians03. 37
    • 4. 08 Skor Z dan Aturan Empiris02. 14
    • 4. 09 Koefisien Variasi dan Penerapannya02. 06
    • 4. 10 Ukuran Bentuk02. 39
    • 4. 11 Meringkas Data02. 03
    • 4. 12 Rekap00. 54
    • 4. 13 Studi Kasus Satu. Statistik deskriptif05. 51

  • Pelajaran 05. Visualisasi data

    20. 55Pratinjau
    • 5. 01 Tujuan Pembelajaran00. 57
    • 5. 02 Visualisasi Data02. 15
    • 5. 03 Bagan Dasar01. 52
    • 5. 04 Bagan Tingkat Lanjut02. 19
    • 5. 05 Interpretasi Grafik02. 57
    • 5. 06 Memilih Bagan yang Sesuai02. 25
    • 5. 07 Bagan Lakukan dan Jangan Lakukan02. 47
    • 5. 08 Bercerita Dengan Bagan01. 29
    • 5. 09 Rekap00. 50
    • 5. 10 Studi Kasus Dua. Visualisasi data03. 04

  • Pelajaran 06. Kemungkinan

    19. 49Pratinjau
    • 6. 01 Tujuan Pembelajaran00. 55
    • 6. 02 Pengantar Probabilitas03. 10
    • 6. 03 Istilah Kunci dalam Probabilitas02. 25
    • 6. 04 Probabilitas Bersyarat02. 11
    • 6. 05 Jenis Acara. Mandiri dan Tergantung02. 59
    • 6. 06 Penambahan Teorema Probabilitas01. 58
    • 6. 07 Teorema Probabilitas Perkalian02. 08
    • 6. 08 Teorema Bayes03. 10
    • 6. 09 Rekap00. 53

  • Pelajaran 07. Distribusi Probabilitas

    23. 20Pratinjau
    • 7. 01 Tujuan Pembelajaran00. 52
    • 7. 02 Variabel Acak02. 21
    • 7. 03 Distribusi Probabilitas Diskrit vs. Kontinu. Bagian A01. 44
    • 7. 04 Distribusi Probabilitas Diskrit vs. Kontinu. Bagian B01. 45
    • 7. 05 Distribusi Probabilitas Diskrit yang Umum Digunakan. Bagian A03. 18
    • 7. 06 Distribusi Probabilitas Diskrit. Poisson03. 16
    • 7. 07 Binomial dengan Teorema Poisson02. 28
    • 7. 08 Distribusi Probabilitas Berkelanjutan yang Umum Digunakan03. 22
    • 7. 09 Penerapan Distribusi Normal02. 49
    • 7. 10 Rekap01. 25

  • Pelajaran 08. Teknik Pengambilan Sampel dan Pengambilan Sampel

    30. 53Pratinjau
    • 8. 01 Tujuan Pembelajaran00. 51
    • 8. 02 Pengantar Sampling dan Kesalahan Sampling03. 05
    • 8. 03 Kelebihan dan Kekurangan Pengambilan Sampel01. 31
    • 8. 04 Metode Sampling Probabilitas. Bagian A02. 32
    • 8. 05 Metode Sampling Probabilitas. Bagian B02. 27
    • 8. 06 Metode Sampling Non-Probabilitas. Bagian A01. 42
    • 8. 07 Metode Sampling Non-Probabilitas. Bagian B01. 25
    • 8. 08 Penggunaan Probability Sampling dan Non-Probability Sampling02. 08
    • 8. 09 Pengambilan sampel01. 08
    • 8. 10 Distribusi Probabilitas02. 53
    • 8. 11 Teorema Lima Titik Satu00. 52
    • 8. 12 Teorema Limit Pusat02. 14
    • 8. 13 Rekap01. 07
    • 8. 14 Studi Kasus Tiga. Sampel dan Teknik Pengambilan Sampel05. 16
    • 8. 15 Sorotan01. 42

  • Pelajaran 09. Statistik Inferensial

    33. 59Pratinjau
    • 9. 01 Tujuan Pembelajaran01. 04
    • 9. 02 Hipotesis dan Pengujian Hipotesis dalam Bisnis03. 24
    • 9. 03 Hipotesis Null dan Alternatif01. 44
    • 9. 04 Nilai P03. 22
    • 9. 05 Tingkat Signifikansi01. 16
    • 9. 06 Ketik Kesalahan Satu dan Dua01. 37
    • 9. 07 Uji Z02. 24
    • 9. 08 Interval Keyakinan dan Tingkat Signifikansi Persentase. Bagian A02. 52
    • 9. 09 Interval Keyakinan. Bagian B01. 20
    • 9. 10 Tes Satu Ekor dan Dua Ekor04. 43
    • 9. 11 Catatan untuk Diingat untuk Hipotesis Nol01. 02
    • 9. 12 Hipotesis Alternatif01. 51
    • 9. 13 Rekap00. 56
    • 9. 14 Studi Kasus 4. Statistik Inferensial06. 24
    • Pengujian Hipotesis

  • Pelajaran 10. Penerapan Statistik Inferensial

    27. 20Pratinjau
    • 10. 01 Tujuan Pembelajaran00. 50
    • 10. 02 Analisis Bivariat02. 01
    • 10. 03 Memilih Tes yang Sesuai untuk EDA02. 29
    • 10. 04 Parametrik vs. Tes Non-Parametrik01. 54
    • 10. 05 Uji Signifikansi01. 38
    • 10. 06 Uji Z04. 27
    • 10. 07 Tes T00. 54
    • 10. 08 Tes Parametrik ANOVA03. 26
    • 10. 09 Uji Chi-Square02. 31
    • 10. 10 Tes Tanda01. 58
    • 10. 11 Tes Kruskal Wallis01. 04
    • 10. 12 Uji Mann Whitney Wilcoxon01. 18
    • 10. 13 Uji Jalankan untuk Keacakan01. 53
    • 10. 14 Rekap00. 57

  • Pelajaran 11. Hubungan antar Variabel

    18. 08Pratinjau
    • 11. 01 Tujuan Pembelajaran01. 06
    • 11. 02 Korelasi01. 54
    • 11. 03 Koefisien Korelasi Karl Pearson02. 36
    • 11. 04 Karl Pearsons. Gunakan Kasus01. 30
    • 11. 05 Koefisien Korelasi Peringkat Spearman02. 14
    • 11. 06 Penyebab01. 47
    • 11. 07 Contoh Regresi02. 28
    • 11. 08 Koefisien Determinasi01. 12
    • 11. 09 Mengukur Kualitas02. 29
    • 11. 10 Rekap00. 52

  • Pelajaran 12. Penerapan Statistik dalam Bisnis

    17. 25Pratinjau
    • 12. 01 Tujuan Pembelajaran00. 53
    • 12. 02 Cara Menggunakan Statistik Dalam Bisnis Sehari-Hari03. 29
    • 12. 03 Contoh. Bagaimana Tidak Berbohong Dengan Statistik02. 34
    • 12. 04 Bagaimana Tidak Berbohong Dengan Statistik01. 49
    • 12. 05 Berbohong Melalui Visualisasi02. 15
    • 12. 06 Lying About Relationships03. 31
    • 12. 07 Recap01. 06
    • 12. 08 Spotlight01. 48

  • Lesson 13. Assisted Practice

    11. 47Preview
    • Assisted Practice. Problem Statement02. 10
    • Assisted Practice. Solution09. 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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  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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  The sessions are very interesting and easy to understand. I enjoyed each and every one of them, thanks to the trainer, Prashant

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  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

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  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

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  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

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  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

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• Develop skills for real career growthCutting-edge curriculum designed in guidance with industry and academia to develop job-ready skills
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• 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.